diff --git a/docs/paper/reductions.typ b/docs/paper/reductions.typ
index 01372f7cb..80af85123 100644
--- a/docs/paper/reductions.typ
+++ b/docs/paper/reductions.typ
@@ -110,6 +110,7 @@
   "PartitionIntoTriangles": [Partition Into Triangles],
   "FlowShopScheduling": [Flow Shop Scheduling],
   "MinimumTardinessSequencing": [Minimum Tardiness Sequencing],
+  "SequencingToMinimizeMaximumCumulativeCost": [Sequencing to Minimize Maximum Cumulative Cost],
   "SequencingWithinIntervals": [Sequencing Within Intervals],
   "DirectedTwoCommodityIntegralFlow": [Directed Two-Commodity Integral Flow],
 )
@@ -2279,6 +2280,82 @@ NP-completeness was established by Garey, Johnson, and Stockmeyer @gareyJohnsonS
   ]
 }
 
+#{
+  let x = load-model-example("SequencingToMinimizeMaximumCumulativeCost")
+  let costs = x.instance.costs
+  let precs = x.instance.precedences
+  let bound = x.instance.bound
+  let ntasks = costs.len()
+  let sample = x.samples.at(0)
+  let satisfying-count = x.optimal.len()
+  let lehmer = sample.config
+  let schedule = {
+    let avail = range(ntasks)
+    let result = ()
+    for c in lehmer {
+      result.push(avail.at(c))
+      avail = avail.enumerate().filter(((i, v)) => i != c).map(((i, v)) => v)
+    }
+    result
+  }
+  let prefix-sums = {
+    let running = 0
+    let result = ()
+    for task in schedule {
+      running += costs.at(task)
+      result.push(running)
+    }
+    result
+  }
+  [
+    #problem-def("SequencingToMinimizeMaximumCumulativeCost")[
+      Given a set $T$ of $n$ tasks, a precedence relation $prec.eq$ on $T$, an integer cost function $c: T -> ZZ$ (negative values represent profits), and a bound $K in ZZ$, determine whether there exists a one-machine schedule $sigma: T -> {1, 2, dots, n}$ that respects the precedence constraints and satisfies
+      $sum_(sigma(t') lt.eq sigma(t)) c(t') lt.eq K$
+      for every task $t in T$.
+    ][
+      Sequencing to Minimize Maximum Cumulative Cost is the scheduling problem SS7 in Garey & Johnson @garey1979. It is NP-complete by transformation from Register Sufficiency, even when every task cost is in ${-1, 0, 1}$ @garey1979. The problem models precedence-constrained task systems with resource consumption and release, where a negative cost corresponds to a profit or resource refund accumulated as the schedule proceeds.
+
+      When the precedence constraints form a series-parallel digraph, #cite(<abdelWahabKameda1978>, form: "prose") gave a polynomial-time algorithm running in $O(n^2)$ time. #cite(<monmaSidney1979>, form: "prose") placed the problem in a broader family of sequencing objectives solvable efficiently on series-parallel precedence structures. The implementation here uses Lehmer-code enumeration of task orders, so the direct exact search induced by the model runs in $O(n!)$ time.
+
+      *Example.* Consider $n = #ntasks$ tasks with costs $(#costs.map(c => str(c)).join(", "))$, precedence constraints #{precs.map(p => [$t_#(p.at(0) + 1) prec.eq t_#(p.at(1) + 1)$]).join(", ")}, and bound $K = #bound$. The sample schedule $(#schedule.map(t => $t_#(t + 1)$).join(", "))$ has cumulative sums $(#prefix-sums.map(v => str(v)).join(", "))$, so every prefix stays at or below $K = #bound$. Our brute-force solver finds exactly #satisfying-count satisfying schedules for this instance.
+
+      #figure(
+        {
+          let pos = rgb("#f28e2b")
+          let neg = rgb("#76b7b2")
+          let zero = rgb("#bab0ab")
+          align(center, stack(dir: ttb, spacing: 0.35cm,
+            stack(dir: ltr, spacing: 0.08cm,
+              ..schedule.enumerate().map(((i, task)) => {
+                let cost = costs.at(task)
+                let fill = if cost > 0 {
+                  pos.transparentize(70%)
+                } else if cost < 0 {
+                  neg.transparentize(65%)
+                } else {
+                  zero.transparentize(65%)
+                }
+                stack(dir: ttb, spacing: 0.05cm,
+                  box(width: 1.0cm, height: 0.6cm, fill: fill, stroke: 0.4pt + luma(120),
+                    align(center + horizon, text(8pt, weight: "bold")[$t_#(task + 1)$])),
+                  text(6pt, if cost >= 0 { $+ #cost$ } else { $#cost$ }),
+                )
+              }),
+            ),
+            stack(dir: ltr, spacing: 0.08cm,
+              ..prefix-sums.map(v => {
+                box(width: 1.0cm, align(center + horizon, text(7pt)[$#v$]))
+              }),
+            ),
+            text(7pt, [prefix sums after each scheduled task]),
+          ))
+        },
+        caption: [A satisfying schedule for Sequencing to Minimize Maximum Cumulative Cost. Orange boxes add cost, teal boxes release cost, and the displayed prefix sums $(#prefix-sums.map(v => str(v)).join(", "))$ never exceed $K = #bound$.],
+      ) <fig:seq-max-cumulative>
+    ]
+  ]
+}
+
 #problem-def("DirectedTwoCommodityIntegralFlow")[
   Given a directed graph $G = (V, A)$ with arc capacities $c: A -> ZZ^+$, two source-sink pairs $(s_1, t_1)$ and $(s_2, t_2)$, and requirements $R_1, R_2 in ZZ^+$, determine whether there exist two integral flow functions $f_1, f_2: A -> ZZ_(>= 0)$ such that (1) $f_1(a) + f_2(a) <= c(a)$ for all $a in A$, (2) flow $f_i$ is conserved at every vertex except $s_1, s_2, t_1, t_2$, and (3) the net flow into $t_i$ under $f_i$ is at least $R_i$ for $i in {1, 2}$.
 ][
diff --git a/docs/paper/references.bib b/docs/paper/references.bib
index 6482b48a2..abfe2ae72 100644
--- a/docs/paper/references.bib
+++ b/docs/paper/references.bib
@@ -85,6 +85,28 @@ @article{evenItaiShamir1976
   doi     = {10.1137/0205048}
 }
 
+@article{abdelWahabKameda1978,
+  author  = {H. M. Abdel-Wahab and T. Kameda},
+  title   = {Scheduling to Minimize Maximum Cumulative Cost Subject to Series-Parallel Precedence Constraints},
+  journal = {Operations Research},
+  volume  = {26},
+  number  = {1},
+  pages   = {141--158},
+  year    = {1978},
+  doi     = {10.1287/opre.26.1.141}
+}
+
+@article{monmaSidney1979,
+  author  = {Clyde L. Monma and Jeffrey B. Sidney},
+  title   = {Sequencing with Series-Parallel Precedence Constraints},
+  journal = {Mathematics of Operations Research},
+  volume  = {4},
+  number  = {3},
+  pages   = {215--224},
+  year    = {1979},
+  doi     = {10.1287/moor.4.3.215}
+}
+
 @article{glover2019,
   author  = {Fred Glover and Gary Kochenberger and Yu Du},
   title   = {Quantum Bridge Analytics {I}: a tutorial on formulating and using {QUBO} models},
diff --git a/problemreductions-cli/src/cli.rs b/problemreductions-cli/src/cli.rs
index e71a9e426..e7b06d8a6 100644
--- a/problemreductions-cli/src/cli.rs
+++ b/problemreductions-cli/src/cli.rs
@@ -249,6 +249,7 @@ Flags by problem type:
   FVS                             --arcs [--weights] [--num-vertices]
   FlowShopScheduling              --task-lengths, --deadline [--num-processors]
   MinimumTardinessSequencing      --n, --deadlines [--precedence-pairs]
+  SequencingToMinimizeMaximumCumulativeCost --costs, --bound [--precedence-pairs]
   SCS                             --strings, --bound [--alphabet-size]
   D2CIF                           --arcs, --capacities, --source-1, --sink-1, --source-2, --sink-2, --requirement-1, --requirement-2
   ILP, CircuitSAT                 (via reduction only)
@@ -435,6 +436,9 @@ pub struct CreateArgs {
     /// Input strings for LCS (e.g., "ABAC;BACA") or SCS (e.g., "0,1,2;1,2,0")
     #[arg(long)]
     pub strings: Option<String>,
+    /// Task costs for SequencingToMinimizeMaximumCumulativeCost (comma-separated, e.g., "2,-1,3,-2,1,-3")
+    #[arg(long, allow_hyphen_values = true)]
+    pub costs: Option<String>,
     /// Directed arcs for directed graph problems (e.g., 0>1,1>2,2>0)
     #[arg(long)]
     pub arcs: Option<String>,
diff --git a/problemreductions-cli/src/commands/create.rs b/problemreductions-cli/src/commands/create.rs
index 03220f4f6..382199728 100644
--- a/problemreductions-cli/src/commands/create.rs
+++ b/problemreductions-cli/src/commands/create.rs
@@ -14,7 +14,8 @@ use problemreductions::models::graph::{
 };
 use problemreductions::models::misc::{
     BinPacking, FlowShopScheduling, LongestCommonSubsequence, MinimumTardinessSequencing,
-    PaintShop, SequencingWithinIntervals, ShortestCommonSupersequence, SubsetSum,
+    PaintShop, SequencingToMinimizeMaximumCumulativeCost, SequencingWithinIntervals,
+    ShortestCommonSupersequence, SubsetSum,
 };
 use problemreductions::prelude::*;
 use problemreductions::registry::collect_schemas;
@@ -76,6 +77,7 @@ fn all_data_flags_empty(args: &CreateArgs) -> bool {
         && args.bound.is_none()
         && args.pattern.is_none()
         && args.strings.is_none()
+        && args.costs.is_none()
         && args.arcs.is_none()
         && args.deadlines.is_none()
         && args.precedence_pairs.is_none()
@@ -185,6 +187,50 @@ fn resolve_rule_example(
         })
 }
 
+fn parse_precedence_pairs(raw: Option<&str>) -> Result<Vec<(usize, usize)>> {
+    raw.filter(|s| !s.is_empty())
+        .map(|s| {
+            s.split(',')
+                .map(|pair| {
+                    let pair = pair.trim();
+                    let (pred, succ) = pair.split_once('>').ok_or_else(|| {
+                        anyhow::anyhow!(
+                            "Invalid --precedence-pairs value '{}': expected 'u>v'",
+                            pair
+                        )
+                    })?;
+                    let pred = pred.trim().parse::<usize>().map_err(|_| {
+                        anyhow::anyhow!(
+                            "Invalid --precedence-pairs value '{}': expected 'u>v' with nonnegative integer indices",
+                            pair
+                        )
+                    })?;
+                    let succ = succ.trim().parse::<usize>().map_err(|_| {
+                        anyhow::anyhow!(
+                            "Invalid --precedence-pairs value '{}': expected 'u>v' with nonnegative integer indices",
+                            pair
+                        )
+                    })?;
+                    Ok((pred, succ))
+                })
+                .collect()
+        })
+        .unwrap_or_else(|| Ok(vec![]))
+}
+
+fn validate_precedence_pairs(precedences: &[(usize, usize)], num_tasks: usize) -> Result<()> {
+    for &(pred, succ) in precedences {
+        anyhow::ensure!(
+            pred < num_tasks && succ < num_tasks,
+            "precedence index out of range: ({}, {}) but num_tasks = {}",
+            pred,
+            succ,
+            num_tasks
+        );
+    }
+    Ok(())
+}
+
 fn create_from_example(args: &CreateArgs, out: &OutputConfig) -> Result<()> {
     let example_spec = args
         .example
@@ -233,7 +279,6 @@ fn type_format_hint(type_name: &str, graph_type: Option<&str>) -> &'static str {
         "Vec<Vec<usize>>" => "semicolon-separated groups: \"0,1;2,3\"",
         "usize" => "integer",
         "u64" => "integer",
-        "Vec<u64>" => "comma-separated integers: 0,0,5",
         "i64" => "integer",
         "BigUint" => "nonnegative decimal integer",
         "Vec<BigUint>" => "comma-separated nonnegative decimal integers: 3,7,1,8",
@@ -296,6 +341,9 @@ fn example_for(canonical: &str, graph_type: Option<&str>) -> &'static str {
         "SubsetSum" => "--sizes 3,7,1,8,2,4 --target 11",
         "SetBasis" => "--universe 4 --sets \"0,1;1,2;0,2;0,1,2\" --k 3",
         "ShortestCommonSupersequence" => "--strings \"0,1,2;1,2,0\" --bound 4",
+        "SequencingToMinimizeMaximumCumulativeCost" => {
+            "--costs 2,-1,3,-2,1,-3 --precedence-pairs \"0>2,1>2,1>3,2>4,3>5,4>5\" --bound 4"
+        }
         _ => "",
     }
 }
@@ -340,6 +388,41 @@ fn parse_nonnegative_usize_bound(bound: i64, problem_name: &str, usage: &str) ->
         .map_err(|_| anyhow::anyhow!("{problem_name} requires nonnegative --bound\n\n{usage}"))
 }
 
+fn validate_sequencing_within_intervals_inputs(
+    release_times: &[u64],
+    deadlines: &[u64],
+    lengths: &[u64],
+    usage: &str,
+) -> Result<()> {
+    if release_times.len() != deadlines.len() {
+        bail!("release_times and deadlines must have the same length\n\n{usage}");
+    }
+    if release_times.len() != lengths.len() {
+        bail!("release_times and lengths must have the same length\n\n{usage}");
+    }
+
+    for (i, ((&release_time, &deadline), &length)) in release_times
+        .iter()
+        .zip(deadlines.iter())
+        .zip(lengths.iter())
+        .enumerate()
+    {
+        let end = release_time.checked_add(length).ok_or_else(|| {
+            anyhow::anyhow!("Task {i}: overflow computing r(i) + l(i)\n\n{usage}")
+        })?;
+        if end > deadline {
+            bail!(
+                "Task {i}: r({}) + l({}) > d({}), time window is empty\n\n{usage}",
+                release_time,
+                length,
+                deadline
+            );
+        }
+    }
+
+    Ok(())
+}
+
 fn print_problem_help(canonical: &str, graph_type: Option<&str>) -> Result<()> {
     let is_geometry = matches!(
         graph_type,
@@ -1175,39 +1258,14 @@ pub fn create(args: &CreateArgs, out: &OutputConfig) -> Result<()> {
                 )
             })?;
             let deadlines: Vec<usize> = util::parse_comma_list(deadlines_str)?;
-            let precedences: Vec<(usize, usize)> = match args.precedence_pairs.as_deref() {
-                Some(s) if !s.is_empty() => s
-                    .split(',')
-                    .map(|pair| {
-                        let parts: Vec<&str> = pair.trim().split('>').collect();
-                        anyhow::ensure!(
-                            parts.len() == 2,
-                            "Invalid precedence format '{}', expected 'u>v'",
-                            pair.trim()
-                        );
-                        Ok((
-                            parts[0].trim().parse::<usize>()?,
-                            parts[1].trim().parse::<usize>()?,
-                        ))
-                    })
-                    .collect::<Result<Vec<_>>>()?,
-                _ => vec![],
-            };
+            let precedences = parse_precedence_pairs(args.precedence_pairs.as_deref())?;
             anyhow::ensure!(
                 deadlines.len() == num_tasks,
                 "deadlines length ({}) must equal num_tasks ({})",
                 deadlines.len(),
                 num_tasks
             );
-            for &(pred, succ) in &precedences {
-                anyhow::ensure!(
-                    pred < num_tasks && succ < num_tasks,
-                    "precedence index out of range: ({}, {}) but num_tasks = {}",
-                    pred,
-                    succ,
-                    num_tasks
-                );
-            }
+            validate_precedence_pairs(&precedences, num_tasks)?;
             (
                 ser(MinimumTardinessSequencing::new(
                     num_tasks,
@@ -1218,6 +1276,33 @@ pub fn create(args: &CreateArgs, out: &OutputConfig) -> Result<()> {
             )
         }
 
+        // SequencingToMinimizeMaximumCumulativeCost
+        "SequencingToMinimizeMaximumCumulativeCost" => {
+            let costs_str = args.costs.as_deref().ok_or_else(|| {
+                anyhow::anyhow!(
+                    "SequencingToMinimizeMaximumCumulativeCost requires --costs\n\n\
+                     Usage: pred create SequencingToMinimizeMaximumCumulativeCost --costs 2,-1,3,-2,1,-3 --precedence-pairs \"0>2,1>2,1>3,2>4,3>5,4>5\" --bound 4"
+                )
+            })?;
+            let bound = args.bound.ok_or_else(|| {
+                anyhow::anyhow!(
+                    "SequencingToMinimizeMaximumCumulativeCost requires --bound\n\n\
+                     Usage: pred create SequencingToMinimizeMaximumCumulativeCost --costs 2,-1,3,-2,1,-3 --precedence-pairs \"0>2,1>2,1>3,2>4,3>5,4>5\" --bound 4"
+                )
+            })?;
+            let costs: Vec<i64> = util::parse_comma_list(costs_str)?;
+            let precedences = parse_precedence_pairs(args.precedence_pairs.as_deref())?;
+            validate_precedence_pairs(&precedences, costs.len())?;
+            (
+                ser(SequencingToMinimizeMaximumCumulativeCost::new(
+                    costs,
+                    precedences,
+                    bound,
+                ))?,
+                resolved_variant.clone(),
+            )
+        }
+
         // SequencingWithinIntervals
         "SequencingWithinIntervals" => {
             let usage = "Usage: pred create SequencingWithinIntervals --release-times 0,0,5 --deadlines 11,11,6 --lengths 3,1,1";
@@ -1233,6 +1318,12 @@ pub fn create(args: &CreateArgs, out: &OutputConfig) -> Result<()> {
             let release_times: Vec<u64> = util::parse_comma_list(rt_str)?;
             let deadlines: Vec<u64> = util::parse_comma_list(dl_str)?;
             let lengths: Vec<u64> = util::parse_comma_list(len_str)?;
+            validate_sequencing_within_intervals_inputs(
+                &release_times,
+                &deadlines,
+                &lengths,
+                usage,
+            )?;
             (
                 ser(SequencingWithinIntervals::new(
                     release_times,
diff --git a/problemreductions-cli/tests/cli_tests.rs b/problemreductions-cli/tests/cli_tests.rs
index 9c35fd001..ef6f41cdd 100644
--- a/problemreductions-cli/tests/cli_tests.rs
+++ b/problemreductions-cli/tests/cli_tests.rs
@@ -4068,6 +4068,150 @@ fn test_create_factoring_missing_bits() {
     );
 }
 
+#[test]
+fn test_create_sequencing_to_minimize_maximum_cumulative_cost() {
+    let output = pred()
+        .args([
+            "create",
+            "SequencingToMinimizeMaximumCumulativeCost",
+            "--costs",
+            "2,-1,3,-2,1,-3",
+            "--precedence-pairs",
+            "0>2,1>2,1>3,2>4,3>5,4>5",
+            "--bound",
+            "4",
+        ])
+        .output()
+        .unwrap();
+    assert!(
+        output.status.success(),
+        "stderr: {}",
+        String::from_utf8_lossy(&output.stderr)
+    );
+    let stdout = String::from_utf8(output.stdout).unwrap();
+    let json: serde_json::Value = serde_json::from_str(&stdout).unwrap();
+    assert_eq!(json["type"], "SequencingToMinimizeMaximumCumulativeCost");
+    assert_eq!(
+        json["data"]["costs"],
+        serde_json::json!([2, -1, 3, -2, 1, -3])
+    );
+    assert_eq!(
+        json["data"]["precedences"],
+        serde_json::json!([[0, 2], [1, 2], [1, 3], [2, 4], [3, 5], [4, 5]])
+    );
+    assert_eq!(json["data"]["bound"], 4);
+}
+
+#[test]
+fn test_create_sequencing_to_minimize_maximum_cumulative_cost_no_flags_shows_help() {
+    let output = pred()
+        .args(["create", "SequencingToMinimizeMaximumCumulativeCost"])
+        .output()
+        .unwrap();
+    assert!(
+        !output.status.success(),
+        "should exit non-zero when showing help without data flags"
+    );
+    let stderr = String::from_utf8_lossy(&output.stderr);
+    assert!(
+        stderr.contains("--costs"),
+        "expected '--costs' in help output, got: {stderr}"
+    );
+    assert!(
+        stderr.contains("--bound"),
+        "expected '--bound' in help output, got: {stderr}"
+    );
+}
+
+#[test]
+fn test_create_sequencing_to_minimize_maximum_cumulative_cost_missing_costs() {
+    let output = pred()
+        .args([
+            "create",
+            "SequencingToMinimizeMaximumCumulativeCost",
+            "--bound",
+            "4",
+        ])
+        .output()
+        .unwrap();
+    assert!(!output.status.success());
+    let stderr = String::from_utf8_lossy(&output.stderr);
+    assert!(
+        stderr.contains("requires --costs"),
+        "expected missing --costs message, got: {stderr}"
+    );
+}
+
+#[test]
+fn test_create_sequencing_to_minimize_maximum_cumulative_cost_bad_precedence() {
+    let output = pred()
+        .args([
+            "create",
+            "SequencingToMinimizeMaximumCumulativeCost",
+            "--costs",
+            "1,-1,2",
+            "--precedence-pairs",
+            "0>3",
+            "--bound",
+            "2",
+        ])
+        .output()
+        .unwrap();
+    assert!(!output.status.success());
+    let stderr = String::from_utf8_lossy(&output.stderr);
+    assert!(
+        stderr.contains("precedence"),
+        "expected precedence validation error, got: {stderr}"
+    );
+}
+
+#[test]
+fn test_create_sequencing_to_minimize_maximum_cumulative_cost_invalid_precedence_pair() {
+    let output = pred()
+        .args([
+            "create",
+            "SequencingToMinimizeMaximumCumulativeCost",
+            "--costs",
+            "1,-1,2",
+            "--precedence-pairs",
+            "a>b",
+            "--bound",
+            "2",
+        ])
+        .output()
+        .unwrap();
+    assert!(!output.status.success());
+    let stderr = String::from_utf8_lossy(&output.stderr);
+    assert!(
+        stderr.contains("--precedence-pairs"),
+        "expected flag-specific precedence parse error, got: {stderr}"
+    );
+}
+
+#[test]
+fn test_create_sequencing_to_minimize_maximum_cumulative_cost_allows_negative_values() {
+    let output = pred()
+        .args([
+            "create",
+            "SequencingToMinimizeMaximumCumulativeCost",
+            "--costs",
+            "-1,2,-3",
+            "--bound",
+            "-1",
+        ])
+        .output()
+        .unwrap();
+    assert!(
+        output.status.success(),
+        "stderr: {}",
+        String::from_utf8_lossy(&output.stderr)
+    );
+    let stdout = String::from_utf8(output.stdout).unwrap();
+    let json: serde_json::Value = serde_json::from_str(&stdout).unwrap();
+    assert_eq!(json["data"]["costs"], serde_json::json!([-1, 2, -3]));
+    assert_eq!(json["data"]["bound"], -1);
+}
+
 // ---- Timeout tests (H3) ----
 
 #[test]
@@ -4848,4 +4992,67 @@ fn test_create_sequencing_within_intervals_rejects_empty_window() {
         .output()
         .unwrap();
     assert!(!output.status.success());
+    let stderr = String::from_utf8_lossy(&output.stderr);
+    assert!(
+        !stderr.contains("panicked at"),
+        "expected graceful CLI error, got panic: {stderr}"
+    );
+    assert!(
+        stderr.contains("time window is empty"),
+        "expected empty-window validation error, got: {stderr}"
+    );
+}
+
+#[test]
+fn test_create_sequencing_within_intervals_rejects_mismatched_lengths() {
+    let output = pred()
+        .args([
+            "create",
+            "SequencingWithinIntervals",
+            "--release-times",
+            "0,1",
+            "--deadlines",
+            "2",
+            "--lengths",
+            "1,1",
+        ])
+        .output()
+        .unwrap();
+    assert!(!output.status.success());
+    let stderr = String::from_utf8_lossy(&output.stderr);
+    assert!(
+        !stderr.contains("panicked at"),
+        "expected graceful CLI error, got panic: {stderr}"
+    );
+    assert!(
+        stderr.contains("must have the same length"),
+        "expected length validation error, got: {stderr}"
+    );
+}
+
+#[test]
+fn test_create_sequencing_within_intervals_rejects_overflow() {
+    let output = pred()
+        .args([
+            "create",
+            "SequencingWithinIntervals",
+            "--release-times",
+            "18446744073709551615",
+            "--deadlines",
+            "18446744073709551615",
+            "--lengths",
+            "1",
+        ])
+        .output()
+        .unwrap();
+    assert!(!output.status.success());
+    let stderr = String::from_utf8_lossy(&output.stderr);
+    assert!(
+        !stderr.contains("panicked at"),
+        "expected graceful CLI error, got panic: {stderr}"
+    );
+    assert!(
+        stderr.contains("overflow computing r(i) + l(i)"),
+        "expected overflow validation error, got: {stderr}"
+    );
 }
diff --git a/src/example_db/fixtures/examples.json b/src/example_db/fixtures/examples.json
index 6e52883b2..baa53956f 100644
--- a/src/example_db/fixtures/examples.json
+++ b/src/example_db/fixtures/examples.json
@@ -32,6 +32,7 @@
     {"problem":"PartitionIntoTriangles","variant":{"graph":"SimpleGraph"},"instance":{"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,2,null],[1,2,null],[3,4,null],[3,5,null],[4,5,null],[0,3,null]],"node_holes":[],"nodes":[null,null,null,null,null,null]}}},"samples":[{"config":[0,0,0,1,1,1],"metric":true}],"optimal":[{"config":[0,0,0,1,1,1],"metric":true},{"config":[1,1,1,0,0,0],"metric":true}]},
     {"problem":"QUBO","variant":{"weight":"f64"},"instance":{"matrix":[[-1.0,2.0,0.0],[0.0,-1.0,2.0],[0.0,0.0,-1.0]],"num_vars":3},"samples":[{"config":[1,0,1],"metric":{"Valid":-2.0}}],"optimal":[{"config":[1,0,1],"metric":{"Valid":-2.0}}]},
     {"problem":"Satisfiability","variant":{},"instance":{"clauses":[{"literals":[1,2]},{"literals":[-1,3]},{"literals":[-2,-3]}],"num_vars":3},"samples":[{"config":[1,0,1],"metric":true}],"optimal":[{"config":[0,1,0],"metric":true},{"config":[1,0,1],"metric":true}]},
+    {"problem":"SequencingToMinimizeMaximumCumulativeCost","variant":{},"instance":{"bound":4,"costs":[2,-1,3,-2,1,-3],"precedences":[[0,2],[1,2],[1,3],[2,4],[3,5],[4,5]]},"samples":[{"config":[1,0,1,0,0,0],"metric":true}],"optimal":[{"config":[0,0,0,0,0,0],"metric":true},{"config":[0,0,1,0,0,0],"metric":true},{"config":[1,0,0,0,0,0],"metric":true},{"config":[1,0,1,0,0,0],"metric":true},{"config":[1,2,0,0,0,0],"metric":true}]},
     {"problem":"SequencingWithinIntervals","variant":{},"instance":{"deadlines":[11,11,11,11,6],"lengths":[3,1,2,4,1],"release_times":[0,0,0,0,5]},"samples":[{"config":[0,6,3,7,0],"metric":true}],"optimal":[{"config":[0,6,3,7,0],"metric":true},{"config":[0,10,3,6,0],"metric":true},{"config":[2,6,0,7,0],"metric":true},{"config":[2,10,0,6,0],"metric":true},{"config":[6,0,9,1,0],"metric":true},{"config":[6,4,9,0,0],"metric":true},{"config":[8,0,6,1,0],"metric":true},{"config":[8,4,6,0,0],"metric":true}]},
     {"problem":"SetBasis","variant":{},"instance":{"collection":[[0,1],[1,2],[0,2],[0,1,2]],"k":3,"universe_size":4},"samples":[{"config":[1,0,0,0,0,1,0,0,0,0,1,0],"metric":true}],"optimal":[{"config":[0,0,1,0,0,1,0,0,1,0,0,0],"metric":true},{"config":[0,0,1,0,1,0,0,0,0,1,0,0],"metric":true},{"config":[0,1,0,0,0,0,1,0,1,0,0,0],"metric":true},{"config":[0,1,0,0,1,0,0,0,0,0,1,0],"metric":true},{"config":[0,1,1,0,1,0,1,0,1,1,0,0],"metric":true},{"config":[0,1,1,0,1,1,0,0,1,0,1,0],"metric":true},{"config":[1,0,0,0,0,0,1,0,0,1,0,0],"metric":true},{"config":[1,0,0,0,0,1,0,0,0,0,1,0],"metric":true},{"config":[1,0,1,0,0,1,1,0,1,1,0,0],"metric":true},{"config":[1,0,1,0,1,1,0,0,0,1,1,0],"metric":true},{"config":[1,1,0,0,0,1,1,0,1,0,1,0],"metric":true},{"config":[1,1,0,0,1,0,1,0,0,1,1,0],"metric":true}]},
     {"problem":"ShortestCommonSupersequence","variant":{},"instance":{"alphabet_size":3,"bound":4,"strings":[[0,1,2],[1,0,2]]},"samples":[{"config":[1,0,1,2],"metric":true}],"optimal":[{"config":[0,1,0,2],"metric":true},{"config":[1,0,1,2],"metric":true}]},
@@ -50,18 +51,18 @@
     {"source":{"problem":"KColoring","variant":{"graph":"SimpleGraph","k":"KN"},"instance":{"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,4,null],[0,5,null],[1,2,null],[1,6,null],[2,3,null],[2,7,null],[3,4,null],[3,8,null],[4,9,null],[5,7,null],[5,8,null],[6,8,null],[6,9,null],[7,9,null]],"node_holes":[],"nodes":[null,null,null,null,null,null,null,null,null,null]}},"num_colors":3}},"target":{"problem":"ILP","variant":{"variable":"bool"},"instance":{"constraints":[{"cmp":"Eq","rhs":1.0,"terms":[[0,1.0],[1,1.0],[2,1.0]]},{"cmp":"Eq","rhs":1.0,"terms":[[3,1.0],[4,1.0],[5,1.0]]},{"cmp":"Eq","rhs":1.0,"terms":[[6,1.0],[7,1.0],[8,1.0]]},{"cmp":"Eq","rhs":1.0,"terms":[[9,1.0],[10,1.0],[11,1.0]]},{"cmp":"Eq","rhs":1.0,"terms":[[12,1.0],[13,1.0],[14,1.0]]},{"cmp":"Eq","rhs":1.0,"terms":[[15,1.0],[16,1.0],[17,1.0]]},{"cmp":"Eq","rhs":1.0,"terms":[[18,1.0],[19,1.0],[20,1.0]]},{"cmp":"Eq","rhs":1.0,"terms":[[21,1.0],[22,1.0],[23,1.0]]},{"cmp":"Eq","rhs":1.0,"terms":[[24,1.0],[25,1.0],[26,1.0]]},{"cmp":"Eq","rhs":1.0,"terms":[[27,1.0],[28,1.0],[29,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[0,1.0],[3,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[1,1.0],[4,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[2,1.0],[5,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[0,1.0],[12,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[1,1.0],[13,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[2,1.0],[14,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[0,1.0],[15,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[1,1.0],[16,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[2,1.0],[17,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[3,1.0],[6,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[4,1.0],[7,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[5,1.0],[8,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[3,1.0],[18,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[4,1.0],[19,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[5,1.0],[20,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[6,1.0],[9,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[7,1.0],[10,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[8,1.0],[11,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[6,1.0],[21,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[7,1.0],[22,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[8,1.0],[23,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[9,1.0],[12,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[10,1.0],[13,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[11,1.0],[14,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[9,1.0],[24,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[10,1.0],[25,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[11,1.0],[26,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[12,1.0],[27,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[13,1.0],[28,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[14,1.0],[29,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[15,1.0],[21,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[16,1.0],[22,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[17,1.0],[23,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[15,1.0],[24,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[16,1.0],[25,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[17,1.0],[26,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[18,1.0],[24,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[19,1.0],[25,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[20,1.0],[26,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[18,1.0],[27,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[19,1.0],[28,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[20,1.0],[29,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[21,1.0],[27,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[22,1.0],[28,1.0]]},{"cmp":"Le","rhs":1.0,"terms":[[23,1.0],[29,1.0]]}],"num_vars":30,"objective":[],"sense":"Minimize"}},"solutions":[{"source_config":[0,2,0,1,2,1,1,2,0,0],"target_config":[1,0,0,0,0,1,1,0,0,0,1,0,0,0,1,0,1,0,0,1,0,0,0,1,1,0,0,1,0,0]}]},
     {"source":{"problem":"KColoring","variant":{"graph":"SimpleGraph","k":"KN"},"instance":{"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,2,null],[1,3,null],[2,3,null],[2,4,null],[3,4,null]],"node_holes":[],"nodes":[null,null,null,null,null]}},"num_colors":3}},"target":{"problem":"QUBO","variant":{"weight":"f64"},"instance":{"matrix":[[-6.0,12.0,12.0,3.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],[0.0,-6.0,12.0,0.0,3.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],[0.0,0.0,-6.0,0.0,0.0,3.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0],[0.0,0.0,0.0,-6.0,12.0,12.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0],[0.0,0.0,0.0,0.0,-6.0,12.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0],[0.0,0.0,0.0,0.0,0.0,-6.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,-6.0,12.0,12.0,3.0,0.0,0.0,3.0,0.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,-6.0,12.0,0.0,3.0,0.0,0.0,3.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-6.0,0.0,0.0,3.0,0.0,0.0,3.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-6.0,12.0,12.0,3.0,0.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-6.0,12.0,0.0,3.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-6.0,0.0,0.0,3.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-6.0,12.0,12.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-6.0,12.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-6.0]],"num_vars":15}},"solutions":[{"source_config":[1,2,2,1,0],"target_config":[0,1,0,0,0,1,0,0,1,0,1,0,1,0,0]}]},
     {"source":{"problem":"KSatisfiability","variant":{"k":"K2"},"instance":{"clauses":[{"literals":[1,2]},{"literals":[-1,3]},{"literals":[-2,4]},{"literals":[-3,-4]}],"num_vars":4}},"target":{"problem":"QUBO","variant":{"weight":"f64"},"instance":{"matrix":[[0.0,1.0,-1.0,0.0],[0.0,0.0,0.0,-1.0],[0.0,0.0,0.0,1.0],[0.0,0.0,0.0,0.0]],"num_vars":4}},"solutions":[{"source_config":[0,1,0,1],"target_config":[0,1,0,1]}]},
-    {"source":{"problem":"KSatisfiability","variant":{"k":"K3"},"instance":{"clauses":[{"literals":[1,2,-3]},{"literals":[-1,3,4]},{"literals":[2,-4,5]},{"literals":[-2,3,-5]},{"literals":[1,-3,5]},{"literals":[-1,-2,4]},{"literals":[3,-4,-5]}],"num_vars":5}},"target":{"problem":"QUBO","variant":{"weight":"f64"},"instance":{"matrix":[[0.0,4.0,-4.0,0.0,0.0,4.0,-4.0,0.0,0.0,4.0,-4.0,0.0],[0.0,0.0,-2.0,-2.0,0.0,4.0,0.0,4.0,-4.0,0.0,-4.0,0.0],[0.0,0.0,2.0,-2.0,0.0,1.0,4.0,0.0,4.0,-4.0,0.0,4.0],[0.0,0.0,0.0,4.0,0.0,0.0,-1.0,-4.0,0.0,0.0,-1.0,-4.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,-1.0,1.0,-1.0,0.0,1.0],[0.0,0.0,0.0,0.0,0.0,-2.0,0.0,0.0,0.0,0.0,0.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0]],"num_vars":12}},"solutions":[{"source_config":[0,0,0,0,0],"target_config":[0,0,0,0,0,1,0,0,0,0,0,0]}]},
+    {"source":{"problem":"KSatisfiability","variant":{"k":"K3"},"instance":{"clauses":[{"literals":[1,2,-3]},{"literals":[-1,3,4]},{"literals":[2,-4,5]},{"literals":[-2,3,-5]},{"literals":[1,-3,5]},{"literals":[-1,-2,4]},{"literals":[3,-4,-5]}],"num_vars":5}},"target":{"problem":"QUBO","variant":{"weight":"f64"},"instance":{"matrix":[[0.0,4.0,-4.0,0.0,0.0,4.0,-4.0,0.0,0.0,4.0,-4.0,0.0],[0.0,0.0,-2.0,-2.0,0.0,4.0,0.0,4.0,-4.0,0.0,-4.0,0.0],[0.0,0.0,2.0,-2.0,0.0,1.0,4.0,0.0,4.0,-4.0,0.0,4.0],[0.0,0.0,0.0,4.0,0.0,0.0,-1.0,-4.0,0.0,0.0,-1.0,-4.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,-1.0,1.0,-1.0,0.0,1.0],[0.0,0.0,0.0,0.0,0.0,-2.0,0.0,0.0,0.0,0.0,0.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0]],"num_vars":12}},"solutions":[{"source_config":[1,1,1,1,1],"target_config":[1,1,1,1,1,0,0,0,0,0,1,0]}]},
     {"source":{"problem":"KSatisfiability","variant":{"k":"K3"},"instance":{"clauses":[{"literals":[1,2,3]},{"literals":[-1,-2,3]}],"num_vars":3}},"target":{"problem":"SubsetSum","variant":{},"instance":{"sizes":["10010","10001","1010","1001","111","100","10","20","1","2"],"target":"11144"}},"solutions":[{"source_config":[0,0,1],"target_config":[0,1,0,1,1,0,1,1,1,0]}]},
     {"source":{"problem":"KSatisfiability","variant":{"k":"KN"},"instance":{"clauses":[{"literals":[1,-2,3]},{"literals":[-1,3,4]},{"literals":[2,-3,-4]}],"num_vars":4}},"target":{"problem":"Satisfiability","variant":{},"instance":{"clauses":[{"literals":[1,-2,3]},{"literals":[-1,3,4]},{"literals":[2,-3,-4]}],"num_vars":4}},"solutions":[{"source_config":[1,1,1,0],"target_config":[1,1,1,0]}]},
     {"source":{"problem":"Knapsack","variant":{},"instance":{"capacity":7,"values":[3,4,5,7],"weights":[2,3,4,5]}},"target":{"problem":"QUBO","variant":{"weight":"f64"},"instance":{"matrix":[[-483.0,240.0,320.0,400.0,80.0,160.0,320.0],[0.0,-664.0,480.0,600.0,120.0,240.0,480.0],[0.0,0.0,-805.0,800.0,160.0,320.0,640.0],[0.0,0.0,0.0,-907.0,200.0,400.0,800.0],[0.0,0.0,0.0,0.0,-260.0,80.0,160.0],[0.0,0.0,0.0,0.0,0.0,-480.0,320.0],[0.0,0.0,0.0,0.0,0.0,0.0,-800.0]],"num_vars":7}},"solutions":[{"source_config":[1,0,0,1],"target_config":[1,0,0,1,0,0,0]}]},
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@@ -70,18 +71,18 @@
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+    {"source":{"problem":"SpinGlass","variant":{"graph":"SimpleGraph","weight":"i32"},"instance":{"couplings":[1,-1,1,-1,1,-1,1,-1,1,-1,1,-1,1,-1,1],"fields":[0,0,0,0,0,0,0,0,0,0],"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,4,null],[0,5,null],[1,2,null],[1,6,null],[2,3,null],[2,7,null],[3,4,null],[3,8,null],[4,9,null],[5,7,null],[5,8,null],[6,8,null],[6,9,null],[7,9,null]],"node_holes":[],"nodes":[null,null,null,null,null,null,null,null,null,null]}}}},"target":{"problem":"MaxCut","variant":{"graph":"SimpleGraph","weight":"i32"},"instance":{"edge_weights":[1,-1,1,-1,1,-1,1,-1,1,-1,1,-1,1,-1,1],"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,4,null],[0,5,null],[1,2,null],[1,6,null],[2,3,null],[2,7,null],[3,4,null],[3,8,null],[4,9,null],[5,7,null],[5,8,null],[6,8,null],[6,9,null],[7,9,null]],"node_holes":[],"nodes":[null,null,null,null,null,null,null,null,null,null]}}}},"solutions":[{"source_config":[0,1,1,0,0,1,0,0,1,0],"target_config":[0,1,1,0,0,1,0,0,1,0]}]},
+    {"source":{"problem":"TravelingSalesman","variant":{"graph":"SimpleGraph","weight":"i32"},"instance":{"edge_weights":[10,15,20,35,25,30],"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,2,null],[0,3,null],[1,2,null],[1,3,null],[2,3,null]],"node_holes":[],"nodes":[null,null,null,null]}}}},"target":{"problem":"ILP","variant":{"variable":"bool"},"instance":{"constraints":[{"cmp":"Eq","rhs":1.0,"terms":[[0,1.0],[1,1.0],[2,1.0],[3,1.0]]},{"cmp":"Eq","rhs":1.0,"terms":[[4,1.0],[5,1.0],[6,1.0],[7,1.0]]},{"cmp":"Eq","rhs":1.0,"terms":[[8,1.0],[9,1.0],[10,1.0],[11,1.0]]},{"cmp":"Eq","rhs":1.0,"terms":[[12,1.0],[13,1.0],[14,1.0],[15,1.0]]},{"cmp":"Eq","rhs":1.0,"terms":[[0,1.0],[4,1.0],[8,1.0],[12,1.0]]},{"cmp":"Eq","rhs":1.0,"terms":[[1,1.0],[5,1.0],[9,1.0],[13,1.0]]},{"cmp":"Eq","rhs":1.0,"terms":[[2,1.0],[6,1.0],[10,1.0],[14,1.0]]},{"cmp":"Eq","rhs":1.0,"terms":[[3,1.0],[7,1.0],[11,1.0],[15,1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[16,1.0],[0,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[16,1.0],[5,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[16,1.0],[0,-1.0],[5,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[17,1.0],[4,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[17,1.0],[1,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[17,1.0],[4,-1.0],[1,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[18,1.0],[1,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[18,1.0],[6,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[18,1.0],[1,-1.0],[6,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[19,1.0],[5,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[19,1.0],[2,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[19,1.0],[5,-1.0],[2,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[20,1.0],[2,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[20,1.0],[7,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[20,1.0],[2,-1.0],[7,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[21,1.0],[6,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[21,1.0],[3,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[21,1.0],[6,-1.0],[3,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[22,1.0],[3,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[22,1.0],[4,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[22,1.0],[3,-1.0],[4,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[23,1.0],[7,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[23,1.0],[0,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[23,1.0],[7,-1.0],[0,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[24,1.0],[0,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[24,1.0],[9,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[24,1.0],[0,-1.0],[9,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[25,1.0],[8,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[25,1.0],[1,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[25,1.0],[8,-1.0],[1,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[26,1.0],[1,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[26,1.0],[10,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[26,1.0],[1,-1.0],[10,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[27,1.0],[9,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[27,1.0],[2,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[27,1.0],[9,-1.0],[2,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[28,1.0],[2,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[28,1.0],[11,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[28,1.0],[2,-1.0],[11,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[29,1.0],[10,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[29,1.0],[3,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[29,1.0],[10,-1.0],[3,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[30,1.0],[3,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[30,1.0],[8,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[30,1.0],[3,-1.0],[8,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[31,1.0],[11,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[31,1.0],[0,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[31,1.0],[11,-1.0],[0,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[32,1.0],[0,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[32,1.0],[13,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[32,1.0],[0,-1.0],[13,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[33,1.0],[12,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[33,1.0],[1,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[33,1.0],[12,-1.0],[1,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[34,1.0],[1,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[34,1.0],[14,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[34,1.0],[1,-1.0],[14,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[35,1.0],[13,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[35,1.0],[2,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[35,1.0],[13,-1.0],[2,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[36,1.0],[2,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[36,1.0],[15,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[36,1.0],[2,-1.0],[15,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[37,1.0],[14,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[37,1.0],[3,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[37,1.0],[14,-1.0],[3,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[38,1.0],[3,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[38,1.0],[12,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[38,1.0],[3,-1.0],[12,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[39,1.0],[15,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[39,1.0],[0,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[39,1.0],[15,-1.0],[0,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[40,1.0],[4,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[40,1.0],[9,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[40,1.0],[4,-1.0],[9,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[41,1.0],[8,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[41,1.0],[5,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[41,1.0],[8,-1.0],[5,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[42,1.0],[5,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[42,1.0],[10,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[42,1.0],[5,-1.0],[10,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[43,1.0],[9,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[43,1.0],[6,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[43,1.0],[9,-1.0],[6,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[44,1.0],[6,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[44,1.0],[11,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[44,1.0],[6,-1.0],[11,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[45,1.0],[10,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[45,1.0],[7,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[45,1.0],[10,-1.0],[7,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[46,1.0],[7,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[46,1.0],[8,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[46,1.0],[7,-1.0],[8,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[47,1.0],[11,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[47,1.0],[4,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[47,1.0],[11,-1.0],[4,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[48,1.0],[4,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[48,1.0],[13,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[48,1.0],[4,-1.0],[13,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[49,1.0],[12,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[49,1.0],[5,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[49,1.0],[12,-1.0],[5,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[50,1.0],[5,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[50,1.0],[14,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[50,1.0],[5,-1.0],[14,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[51,1.0],[13,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[51,1.0],[6,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[51,1.0],[13,-1.0],[6,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[52,1.0],[6,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[52,1.0],[15,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[52,1.0],[6,-1.0],[15,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[53,1.0],[14,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[53,1.0],[7,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[53,1.0],[14,-1.0],[7,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[54,1.0],[7,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[54,1.0],[12,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[54,1.0],[7,-1.0],[12,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[55,1.0],[15,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[55,1.0],[4,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[55,1.0],[15,-1.0],[4,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[56,1.0],[8,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[56,1.0],[13,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[56,1.0],[8,-1.0],[13,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[57,1.0],[12,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[57,1.0],[9,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[57,1.0],[12,-1.0],[9,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[58,1.0],[9,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[58,1.0],[14,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[58,1.0],[9,-1.0],[14,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[59,1.0],[13,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[59,1.0],[10,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[59,1.0],[13,-1.0],[10,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[60,1.0],[10,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[60,1.0],[15,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[60,1.0],[10,-1.0],[15,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[61,1.0],[14,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[61,1.0],[11,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[61,1.0],[14,-1.0],[11,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[62,1.0],[11,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[62,1.0],[12,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[62,1.0],[11,-1.0],[12,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[63,1.0],[15,-1.0]]},{"cmp":"Le","rhs":0.0,"terms":[[63,1.0],[8,-1.0]]},{"cmp":"Ge","rhs":-1.0,"terms":[[63,1.0],[15,-1.0],[8,-1.0]]}],"num_vars":64,"objective":[[16,10.0],[17,10.0],[18,10.0],[19,10.0],[20,10.0],[21,10.0],[22,10.0],[23,10.0],[24,15.0],[25,15.0],[26,15.0],[27,15.0],[28,15.0],[29,15.0],[30,15.0],[31,15.0],[32,20.0],[33,20.0],[34,20.0],[35,20.0],[36,20.0],[37,20.0],[38,20.0],[39,20.0],[40,35.0],[41,35.0],[42,35.0],[43,35.0],[44,35.0],[45,35.0],[46,35.0],[47,35.0],[48,25.0],[49,25.0],[50,25.0],[51,25.0],[52,25.0],[53,25.0],[54,25.0],[55,25.0],[56,30.0],[57,30.0],[58,30.0],[59,30.0],[60,30.0],[61,30.0],[62,30.0],[63,30.0]],"sense":"Minimize"}},"solutions":[{"source_config":[1,1,0,0,1,1],"target_config":[0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0]}]},
+    {"source":{"problem":"TravelingSalesman","variant":{"graph":"SimpleGraph","weight":"i32"},"instance":{"edge_weights":[1,2,3],"graph":{"inner":{"edge_property":"undirected","edges":[[0,1,null],[0,2,null],[1,2,null]],"node_holes":[],"nodes":[null,null,null]}}}},"target":{"problem":"QUBO","variant":{"weight":"f64"},"instance":{"matrix":[[-14.0,14.0,14.0,14.0,1.0,1.0,14.0,2.0,2.0],[0.0,-14.0,14.0,1.0,14.0,1.0,2.0,14.0,2.0],[0.0,0.0,-14.0,1.0,1.0,14.0,2.0,2.0,14.0],[0.0,0.0,0.0,-14.0,14.0,14.0,14.0,3.0,3.0],[0.0,0.0,0.0,0.0,-14.0,14.0,3.0,14.0,3.0],[0.0,0.0,0.0,0.0,0.0,-14.0,3.0,3.0,14.0],[0.0,0.0,0.0,0.0,0.0,0.0,-14.0,14.0,14.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,-14.0,14.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-14.0]],"num_vars":9}},"solutions":[{"source_config":[1,1,1],"target_config":[0,0,1,0,1,0,1,0,0]}]}
   ]
 }
diff --git a/src/lib.rs b/src/lib.rs
index 42bc6c927..54b2ba0d4 100644
--- a/src/lib.rs
+++ b/src/lib.rs
@@ -58,8 +58,8 @@ pub mod prelude {
     };
     pub use crate::models::misc::{
         BinPacking, Factoring, FlowShopScheduling, Knapsack, LongestCommonSubsequence,
-        MinimumTardinessSequencing, PaintShop, SequencingWithinIntervals,
-        ShortestCommonSupersequence, SubsetSum,
+        MinimumTardinessSequencing, PaintShop, SequencingToMinimizeMaximumCumulativeCost,
+        SequencingWithinIntervals, ShortestCommonSupersequence, SubsetSum,
     };
     pub use crate::models::set::{
         ExactCoverBy3Sets, MaximumSetPacking, MinimumSetCovering, SetBasis,
diff --git a/src/models/misc/mod.rs b/src/models/misc/mod.rs
index c4b125274..5d28c3ad7 100644
--- a/src/models/misc/mod.rs
+++ b/src/models/misc/mod.rs
@@ -8,6 +8,7 @@
 //! - [`LongestCommonSubsequence`]: Longest Common Subsequence
 //! - [`MinimumTardinessSequencing`]: Minimize tardy tasks in single-machine scheduling
 //! - [`PaintShop`]: Minimize color switches in paint shop scheduling
+//! - [`SequencingToMinimizeMaximumCumulativeCost`]: Keep every cumulative schedule cost prefix under a bound
 //! - [`SequencingWithinIntervals`]: Schedule tasks within time windows
 //! - [`ShortestCommonSupersequence`]: Find a common supersequence of bounded length
 //! - [`SubsetSum`]: Find a subset summing to exactly a target value
@@ -19,6 +20,7 @@ mod knapsack;
 mod longest_common_subsequence;
 mod minimum_tardiness_sequencing;
 pub(crate) mod paintshop;
+mod sequencing_to_minimize_maximum_cumulative_cost;
 mod sequencing_within_intervals;
 pub(crate) mod shortest_common_supersequence;
 mod subset_sum;
@@ -30,6 +32,7 @@ pub use knapsack::Knapsack;
 pub use longest_common_subsequence::LongestCommonSubsequence;
 pub use minimum_tardiness_sequencing::MinimumTardinessSequencing;
 pub use paintshop::PaintShop;
+pub use sequencing_to_minimize_maximum_cumulative_cost::SequencingToMinimizeMaximumCumulativeCost;
 pub use sequencing_within_intervals::SequencingWithinIntervals;
 pub use shortest_common_supersequence::ShortestCommonSupersequence;
 pub use subset_sum::SubsetSum;
@@ -42,5 +45,6 @@ pub(crate) fn canonical_model_example_specs() -> Vec<crate::example_db::specs::M
     specs.extend(sequencing_within_intervals::canonical_model_example_specs());
     specs.extend(shortest_common_supersequence::canonical_model_example_specs());
     specs.extend(minimum_tardiness_sequencing::canonical_model_example_specs());
+    specs.extend(sequencing_to_minimize_maximum_cumulative_cost::canonical_model_example_specs());
     specs
 }
diff --git a/src/models/misc/sequencing_to_minimize_maximum_cumulative_cost.rs b/src/models/misc/sequencing_to_minimize_maximum_cumulative_cost.rs
new file mode 100644
index 000000000..9dfdb7629
--- /dev/null
+++ b/src/models/misc/sequencing_to_minimize_maximum_cumulative_cost.rs
@@ -0,0 +1,216 @@
+//! Sequencing to Minimize Maximum Cumulative Cost problem implementation.
+//!
+//! Given a set of tasks with integer costs and precedence constraints, determine
+//! whether there exists a valid one-machine schedule whose running cumulative
+//! cost never exceeds a given bound.
+
+use crate::registry::{FieldInfo, ProblemSchemaEntry};
+use crate::traits::{Problem, SatisfactionProblem};
+use serde::de::Error as _;
+use serde::{Deserialize, Serialize};
+
+inventory::submit! {
+    ProblemSchemaEntry {
+        name: "SequencingToMinimizeMaximumCumulativeCost",
+        display_name: "Sequencing to Minimize Maximum Cumulative Cost",
+        aliases: &[],
+        dimensions: &[],
+        module_path: module_path!(),
+        description: "Schedule tasks with precedence constraints so every cumulative cost prefix stays within a bound",
+        fields: &[
+            FieldInfo { name: "costs", type_name: "Vec<i64>", description: "Task costs in schedule order-independent indexing" },
+            FieldInfo { name: "precedences", type_name: "Vec<(usize, usize)>", description: "Precedence pairs (predecessor, successor)" },
+            FieldInfo { name: "bound", type_name: "i64", description: "Upper bound on every cumulative cost prefix" },
+        ],
+    }
+}
+
+/// Sequencing to Minimize Maximum Cumulative Cost.
+///
+/// Given a set of tasks `T`, a cost `c(t) in Z` for each task, a partial order
+/// on the tasks, and a bound `K`, determine whether there exists a schedule that
+/// respects the precedences and whose running cumulative cost never exceeds `K`.
+///
+/// # Representation
+///
+/// Configurations use Lehmer-code dimensions `[n, n-1, ..., 1]` to encode a
+/// permutation of the task indices.
+#[derive(Debug, Clone, Serialize)]
+pub struct SequencingToMinimizeMaximumCumulativeCost {
+    costs: Vec<i64>,
+    precedences: Vec<(usize, usize)>,
+    bound: i64,
+}
+
+#[derive(Debug, Deserialize)]
+struct SequencingToMinimizeMaximumCumulativeCostUnchecked {
+    costs: Vec<i64>,
+    precedences: Vec<(usize, usize)>,
+    bound: i64,
+}
+
+impl SequencingToMinimizeMaximumCumulativeCost {
+    /// Create a new instance.
+    ///
+    /// # Panics
+    ///
+    /// Panics if any precedence endpoint is out of range.
+    pub fn new(costs: Vec<i64>, precedences: Vec<(usize, usize)>, bound: i64) -> Self {
+        validate_precedences(&precedences, costs.len());
+        Self {
+            costs,
+            precedences,
+            bound,
+        }
+    }
+
+    /// Return the task costs.
+    pub fn costs(&self) -> &[i64] {
+        &self.costs
+    }
+
+    /// Return the precedence constraints.
+    pub fn precedences(&self) -> &[(usize, usize)] {
+        &self.precedences
+    }
+
+    /// Return the cumulative-cost bound.
+    pub fn bound(&self) -> i64 {
+        self.bound
+    }
+
+    /// Return the number of tasks.
+    pub fn num_tasks(&self) -> usize {
+        self.costs.len()
+    }
+
+    /// Return the number of precedence constraints.
+    pub fn num_precedences(&self) -> usize {
+        self.precedences.len()
+    }
+
+    fn decode_schedule(&self, config: &[usize]) -> Option<Vec<usize>> {
+        let n = self.num_tasks();
+        if config.len() != n {
+            return None;
+        }
+
+        let mut available: Vec<usize> = (0..n).collect();
+        let mut schedule = Vec::with_capacity(n);
+        for &digit in config {
+            if digit >= available.len() {
+                return None;
+            }
+            schedule.push(available.remove(digit));
+        }
+        Some(schedule)
+    }
+}
+
+impl<'de> Deserialize<'de> for SequencingToMinimizeMaximumCumulativeCost {
+    fn deserialize<D>(deserializer: D) -> Result<Self, D::Error>
+    where
+        D: serde::Deserializer<'de>,
+    {
+        let unchecked =
+            SequencingToMinimizeMaximumCumulativeCostUnchecked::deserialize(deserializer)?;
+        if let Some(message) =
+            precedence_validation_error(&unchecked.precedences, unchecked.costs.len())
+        {
+            return Err(D::Error::custom(message));
+        }
+        Ok(Self {
+            costs: unchecked.costs,
+            precedences: unchecked.precedences,
+            bound: unchecked.bound,
+        })
+    }
+}
+
+fn validate_precedences(precedences: &[(usize, usize)], num_tasks: usize) {
+    if let Some(message) = precedence_validation_error(precedences, num_tasks) {
+        panic!("{message}");
+    }
+}
+
+fn precedence_validation_error(precedences: &[(usize, usize)], num_tasks: usize) -> Option<String> {
+    for &(pred, succ) in precedences {
+        if pred >= num_tasks {
+            return Some(format!(
+                "predecessor index {} out of range (num_tasks = {})",
+                pred, num_tasks
+            ));
+        }
+        if succ >= num_tasks {
+            return Some(format!(
+                "successor index {} out of range (num_tasks = {})",
+                succ, num_tasks
+            ));
+        }
+    }
+    None
+}
+
+impl Problem for SequencingToMinimizeMaximumCumulativeCost {
+    const NAME: &'static str = "SequencingToMinimizeMaximumCumulativeCost";
+    type Metric = bool;
+
+    fn variant() -> Vec<(&'static str, &'static str)> {
+        crate::variant_params![]
+    }
+
+    fn dims(&self) -> Vec<usize> {
+        let n = self.num_tasks();
+        (0..n).rev().map(|i| i + 1).collect()
+    }
+
+    fn evaluate(&self, config: &[usize]) -> bool {
+        let Some(schedule) = self.decode_schedule(config) else {
+            return false;
+        };
+
+        let mut positions = vec![0usize; self.num_tasks()];
+        for (position, &task) in schedule.iter().enumerate() {
+            positions[task] = position;
+        }
+        for &(pred, succ) in &self.precedences {
+            if positions[pred] >= positions[succ] {
+                return false;
+            }
+        }
+
+        let mut cumulative = 0i64;
+        for &task in &schedule {
+            cumulative += self.costs[task];
+            if cumulative > self.bound {
+                return false;
+            }
+        }
+        true
+    }
+}
+
+impl SatisfactionProblem for SequencingToMinimizeMaximumCumulativeCost {}
+
+crate::declare_variants! {
+    default sat SequencingToMinimizeMaximumCumulativeCost => "factorial(num_tasks)",
+}
+
+#[cfg(feature = "example-db")]
+pub(crate) fn canonical_model_example_specs() -> Vec<crate::example_db::specs::ModelExampleSpec> {
+    vec![crate::example_db::specs::ModelExampleSpec {
+        id: "sequencing_to_minimize_maximum_cumulative_cost",
+        build: || {
+            let problem = SequencingToMinimizeMaximumCumulativeCost::new(
+                vec![2, -1, 3, -2, 1, -3],
+                vec![(0, 2), (1, 2), (1, 3), (2, 4), (3, 5), (4, 5)],
+                4,
+            );
+            crate::example_db::specs::satisfaction_example(problem, vec![vec![1, 0, 1, 0, 0, 0]])
+        },
+    }]
+}
+
+#[cfg(test)]
+#[path = "../../unit_tests/models/misc/sequencing_to_minimize_maximum_cumulative_cost.rs"]
+mod tests;
diff --git a/src/models/misc/sequencing_within_intervals.rs b/src/models/misc/sequencing_within_intervals.rs
index cd7c3efe4..c6a89bac7 100644
--- a/src/models/misc/sequencing_within_intervals.rs
+++ b/src/models/misc/sequencing_within_intervals.rs
@@ -6,6 +6,7 @@
 
 use crate::registry::{FieldInfo, ProblemSchemaEntry};
 use crate::traits::{Problem, SatisfactionProblem};
+use serde::de::Error as _;
 use serde::{Deserialize, Serialize};
 
 inventory::submit! {
@@ -53,7 +54,7 @@ inventory::submit! {
 /// let solution = solver.find_satisfying(&problem);
 /// assert!(solution.is_some());
 /// ```
-#[derive(Debug, Clone, Serialize, Deserialize)]
+#[derive(Debug, Clone, Serialize)]
 pub struct SequencingWithinIntervals {
     /// Release times for each task.
     release_times: Vec<u64>,
@@ -63,6 +64,13 @@ pub struct SequencingWithinIntervals {
     lengths: Vec<u64>,
 }
 
+#[derive(Debug, Deserialize)]
+struct SequencingWithinIntervalsUnchecked {
+    release_times: Vec<u64>,
+    deadlines: Vec<u64>,
+    lengths: Vec<u64>,
+}
+
 impl SequencingWithinIntervals {
     /// Create a new SequencingWithinIntervals problem.
     ///
@@ -70,28 +78,7 @@ impl SequencingWithinIntervals {
     /// Panics if the three vectors have different lengths, or if any task has
     /// `r(i) + l(i) > d(i)` (empty time window).
     pub fn new(release_times: Vec<u64>, deadlines: Vec<u64>, lengths: Vec<u64>) -> Self {
-        assert_eq!(
-            release_times.len(),
-            deadlines.len(),
-            "release_times and deadlines must have the same length"
-        );
-        assert_eq!(
-            release_times.len(),
-            lengths.len(),
-            "release_times and lengths must have the same length"
-        );
-        for i in 0..release_times.len() {
-            let sum = release_times[i]
-                .checked_add(lengths[i])
-                .expect("overflow computing r(i) + l(i)");
-            assert!(
-                sum <= deadlines[i],
-                "Task {i}: r({}) + l({}) > d({}), time window is empty",
-                release_times[i],
-                lengths[i],
-                deadlines[i]
-            );
-        }
+        validate_time_windows(&release_times, &deadlines, &lengths);
         Self {
             release_times,
             deadlines,
@@ -120,6 +107,66 @@ impl SequencingWithinIntervals {
     }
 }
 
+impl<'de> Deserialize<'de> for SequencingWithinIntervals {
+    fn deserialize<D>(deserializer: D) -> Result<Self, D::Error>
+    where
+        D: serde::Deserializer<'de>,
+    {
+        let unchecked = SequencingWithinIntervalsUnchecked::deserialize(deserializer)?;
+        if let Some(message) = time_window_validation_error(
+            &unchecked.release_times,
+            &unchecked.deadlines,
+            &unchecked.lengths,
+        ) {
+            return Err(D::Error::custom(message));
+        }
+        Ok(Self {
+            release_times: unchecked.release_times,
+            deadlines: unchecked.deadlines,
+            lengths: unchecked.lengths,
+        })
+    }
+}
+
+fn validate_time_windows(release_times: &[u64], deadlines: &[u64], lengths: &[u64]) {
+    if let Some(message) = time_window_validation_error(release_times, deadlines, lengths) {
+        panic!("{message}");
+    }
+}
+
+fn time_window_validation_error(
+    release_times: &[u64],
+    deadlines: &[u64],
+    lengths: &[u64],
+) -> Option<String> {
+    if release_times.len() != deadlines.len() {
+        return Some("release_times and deadlines must have the same length".to_string());
+    }
+    if release_times.len() != lengths.len() {
+        return Some("release_times and lengths must have the same length".to_string());
+    }
+
+    for (i, ((&release_time, &deadline), &length)) in release_times
+        .iter()
+        .zip(deadlines.iter())
+        .zip(lengths.iter())
+        .enumerate()
+    {
+        let end = match release_time.checked_add(length) {
+            Some(end) => end,
+            None => return Some(format!("Task {i}: overflow computing r(i) + l(i)")),
+        };
+        if end > deadline {
+            return Some(format!(
+                "Task {i}: r({}) + l({}) > d({}), time window is empty",
+                release_time, length, deadline
+            ));
+        }
+    }
+
+    None
+}
+
 impl Problem for SequencingWithinIntervals {
     const NAME: &'static str = "SequencingWithinIntervals";
     type Metric = bool;
diff --git a/src/models/mod.rs b/src/models/mod.rs
index 072034f02..38fd899b5 100644
--- a/src/models/mod.rs
+++ b/src/models/mod.rs
@@ -22,7 +22,7 @@ pub use graph::{
 };
 pub use misc::{
     BinPacking, Factoring, FlowShopScheduling, Knapsack, LongestCommonSubsequence,
-    MinimumTardinessSequencing, PaintShop, SequencingWithinIntervals, ShortestCommonSupersequence,
-    SubsetSum,
+    MinimumTardinessSequencing, PaintShop, SequencingToMinimizeMaximumCumulativeCost,
+    SequencingWithinIntervals, ShortestCommonSupersequence, SubsetSum,
 };
 pub use set::{ExactCoverBy3Sets, MaximumSetPacking, MinimumSetCovering, SetBasis};
diff --git a/src/unit_tests/models/misc/sequencing_to_minimize_maximum_cumulative_cost.rs b/src/unit_tests/models/misc/sequencing_to_minimize_maximum_cumulative_cost.rs
new file mode 100644
index 000000000..fa6a4823f
--- /dev/null
+++ b/src/unit_tests/models/misc/sequencing_to_minimize_maximum_cumulative_cost.rs
@@ -0,0 +1,143 @@
+use super::*;
+use crate::solvers::{BruteForce, Solver};
+use crate::traits::Problem;
+
+fn issue_example(bound: i64) -> SequencingToMinimizeMaximumCumulativeCost {
+    SequencingToMinimizeMaximumCumulativeCost::new(
+        vec![2, -1, 3, -2, 1, -3],
+        vec![(0, 2), (1, 2), (1, 3), (2, 4), (3, 5), (4, 5)],
+        bound,
+    )
+}
+
+#[test]
+fn test_sequencing_to_minimize_maximum_cumulative_cost_creation() {
+    let problem = issue_example(4);
+    assert_eq!(problem.costs(), &[2, -1, 3, -2, 1, -3]);
+    assert_eq!(
+        problem.precedences(),
+        &[(0, 2), (1, 2), (1, 3), (2, 4), (3, 5), (4, 5)]
+    );
+    assert_eq!(problem.bound(), 4);
+    assert_eq!(problem.num_tasks(), 6);
+    assert_eq!(problem.num_precedences(), 6);
+    assert_eq!(problem.dims(), vec![6, 5, 4, 3, 2, 1]);
+    assert_eq!(
+        <SequencingToMinimizeMaximumCumulativeCost as Problem>::NAME,
+        "SequencingToMinimizeMaximumCumulativeCost"
+    );
+    assert_eq!(
+        <SequencingToMinimizeMaximumCumulativeCost as Problem>::variant(),
+        vec![]
+    );
+}
+
+#[test]
+fn test_sequencing_to_minimize_maximum_cumulative_cost_evaluate_satisfying_issue_order() {
+    let problem = issue_example(4);
+
+    // Task order [1, 0, 3, 2, 4, 5]:
+    // available [0,1,2,3,4,5] -> pick 1
+    // available [0,2,3,4,5] -> pick 0
+    // available [2,3,4,5] -> pick 1
+    // available [2,4,5] -> pick 0
+    // available [4,5] -> pick 0
+    // available [5] -> pick 0
+    let config = vec![1, 0, 1, 0, 0, 0];
+    assert!(problem.evaluate(&config));
+}
+
+#[test]
+fn test_sequencing_to_minimize_maximum_cumulative_cost_evaluate_tight_bound() {
+    let problem = issue_example(3);
+
+    // Identity order [0,1,2,3,4,5] reaches prefix sums 2,1,4,... and should fail for K = 3.
+    assert!(!problem.evaluate(&[0, 0, 0, 0, 0, 0]));
+}
+
+#[test]
+fn test_sequencing_to_minimize_maximum_cumulative_cost_precedence_violation() {
+    let problem = issue_example(10);
+
+    // Task order [2, 0, 1, 3, 4, 5] violates precedence 0 -> 2.
+    assert!(!problem.evaluate(&[2, 0, 0, 0, 0, 0]));
+}
+
+#[test]
+fn test_sequencing_to_minimize_maximum_cumulative_cost_invalid_config() {
+    let problem = issue_example(4);
+    assert!(!problem.evaluate(&[6, 0, 0, 0, 0, 0]));
+    assert!(!problem.evaluate(&[1, 0, 1, 0, 0]));
+    assert!(!problem.evaluate(&[1, 0, 1, 0, 0, 0, 0]));
+}
+
+#[test]
+fn test_sequencing_to_minimize_maximum_cumulative_cost_brute_force_solver() {
+    let problem = issue_example(4);
+    let solver = BruteForce::new();
+    let solution = solver
+        .find_satisfying(&problem)
+        .expect("should find a satisfying schedule");
+    assert!(problem.evaluate(&solution));
+}
+
+#[test]
+fn test_sequencing_to_minimize_maximum_cumulative_cost_unsatisfiable_cycle() {
+    let problem = SequencingToMinimizeMaximumCumulativeCost::new(
+        vec![1, -1, 2],
+        vec![(0, 1), (1, 2), (2, 0)],
+        10,
+    );
+    let solver = BruteForce::new();
+    assert!(solver.find_satisfying(&problem).is_none());
+}
+
+#[test]
+fn test_sequencing_to_minimize_maximum_cumulative_cost_paper_example() {
+    let problem = issue_example(4);
+    let sample_config = vec![1, 0, 1, 0, 0, 0];
+    assert!(problem.evaluate(&sample_config));
+
+    let satisfying = BruteForce::new().find_all_satisfying(&problem);
+    assert_eq!(satisfying.len(), 5);
+    assert!(satisfying.iter().any(|config| config == &sample_config));
+}
+
+#[test]
+fn test_sequencing_to_minimize_maximum_cumulative_cost_empty_instance() {
+    let problem = SequencingToMinimizeMaximumCumulativeCost::new(vec![], vec![], 0);
+    assert_eq!(problem.num_tasks(), 0);
+    assert_eq!(problem.dims(), Vec::<usize>::new());
+    assert!(problem.evaluate(&[]));
+}
+
+#[test]
+#[should_panic(expected = "predecessor index 4 out of range")]
+fn test_sequencing_to_minimize_maximum_cumulative_cost_invalid_precedence_endpoint() {
+    SequencingToMinimizeMaximumCumulativeCost::new(vec![1, -1, 2], vec![(4, 0)], 2);
+}
+
+#[test]
+fn test_sequencing_to_minimize_maximum_cumulative_cost_serialization() {
+    let problem = issue_example(4);
+    let json = serde_json::to_value(&problem).unwrap();
+    let restored: SequencingToMinimizeMaximumCumulativeCost = serde_json::from_value(json).unwrap();
+    assert_eq!(restored.costs(), problem.costs());
+    assert_eq!(restored.precedences(), problem.precedences());
+    assert_eq!(restored.bound(), problem.bound());
+}
+
+#[test]
+fn test_sequencing_to_minimize_maximum_cumulative_cost_deserialize_rejects_invalid_precedence() {
+    let result: Result<SequencingToMinimizeMaximumCumulativeCost, _> =
+        serde_json::from_value(serde_json::json!({
+            "costs": [1, -1, 2],
+            "precedences": [[4, 0]],
+            "bound": 2
+        }));
+    let err = result.unwrap_err().to_string();
+    assert!(
+        err.contains("predecessor index 4 out of range"),
+        "got: {err}"
+    );
+}
diff --git a/src/unit_tests/models/misc/sequencing_within_intervals.rs b/src/unit_tests/models/misc/sequencing_within_intervals.rs
index b3b5d3205..c54745aaa 100644
--- a/src/unit_tests/models/misc/sequencing_within_intervals.rs
+++ b/src/unit_tests/models/misc/sequencing_within_intervals.rs
@@ -153,3 +153,29 @@ fn test_sequencing_within_intervals_invalid_window() {
     // r + l > d: impossible task
     SequencingWithinIntervals::new(vec![5], vec![3], vec![2]);
 }
+
+#[test]
+fn test_sequencing_within_intervals_deserialize_rejects_mismatched_lengths() {
+    let result: Result<SequencingWithinIntervals, _> = serde_json::from_value(serde_json::json!({
+        "release_times": [0, 1],
+        "deadlines": [2],
+        "lengths": [1, 1]
+    }));
+    let err = result.unwrap_err().to_string();
+    assert!(
+        err.contains("release_times and deadlines must have the same length")
+            || err.contains("release_times and lengths must have the same length"),
+        "got: {err}"
+    );
+}
+
+#[test]
+fn test_sequencing_within_intervals_deserialize_rejects_empty_window() {
+    let result: Result<SequencingWithinIntervals, _> = serde_json::from_value(serde_json::json!({
+        "release_times": [5],
+        "deadlines": [3],
+        "lengths": [2]
+    }));
+    let err = result.unwrap_err().to_string();
+    assert!(err.contains("time window is empty"), "got: {err}");
+}