Systems

Daily Systems Trends Report — October 29, 2026: The Intervention Ledger — When Does Stepping In Pay for Itself?

Daily Systems Trends Report — October 29, 2026

The Intervention Ledger — When Does Stepping In Pay for Itself?

BLUF: Today's edition works the unreported remainder of the Wednesday, October 28 arXiv batch (the Thursday announcement had not landed at publish time). Seventeen items, one question: every intervention available to an agent operator — restarting an unpromising run, letting the model think longer, adding more specialist contexts, routing through a cheaper model, compressing group memory, gating tool calls, restricting an API surface — must price itself against the cheapest null action. The batch's headline: the null action wins more often than the industry's instinct suggests. Continuation beats learned restarts in 216 of 224 budget configurations; more thinking tokens buy nothing significant on half the tested benchmarks; 49-fold context growth buys zero unsafe-clearance improvement. But three interventions DO pay —consequence-aware execution gating, one-directional model handoffs, and interface restrictions ahead of agent filters —and one practical deliverable stands out: a static checker for silently broken prompt-caching integration, already fixed upstream in an official SDK.

Edition note: The Wednesday batch held 57 brand-new submissions across cs.DC (8), cs.SE (33) and cs.MA (16), plus cross-lists. Yesterday's report carried the instruction-stack core; roughly 40 items were unreported. This edition covers the 17 highest-value remainder items. All abstracts read from arxiv.org directly; no press releases, no vendor blogs.

1. Continue or Restart? The Limits of Early Failure Prediction in Coding Agents arXiv:2610.35971

Claim: Early stopping monitors can cut agent compute by terminating unpromising coding-agent runs mid-flight.

Foundation: Replaces wall-clock kills and fixed retry budgets with learned run-level early-exit policies —the operations pattern every serving platform eventually reaches for.

Evidence: 147,002 execution traces, four model-framework settings. Within-task discrimination at early landmarks (5k–20k tokens) is at chance: mean AUC 0.50 [0.48, 0.51] for a fine-tuned Qwen3-0.6B monitor and for 20k-token task-conditional rankers. Learned restarting beats matched random restarting by at most 1.98 percentage points –and an exact algebraic decomposition shows the gain comes from between-task allocation (avoiding stops on easy issues), while attempt-level selection contributes –0.11 to +0.24 points. Across 216 of 224 tested budget-threshold configurations, uninterrupted continuation achieves higher solve rates at LOWER mean token cost than both restart policies.

Trade-offs: Restart policies feel like control and are trivial to deploy, which is exactly why they persist: they pay a real restart cost without discriminating between attempts. The paper does establish that between-task (issue-difficulty) prediction works –the signal exists, just not where vendors are looking.

Verdict: The null is the deliverable. Any deployed "kill unpromising run" feature at attempt level is paying for noise. Kill-and-restart belongs at the task level, not the attempt level.

2. Does More Thinking Pay Off? Reasoning Cost and Correctness of LLMs in Code Generation and Bug Fixing arXiv:2610.36952

Claim: Explicit reasoning traces improve code generation and bug fixing, and the improvement is worth the tokens they consume.

Foundation: Replaces correctness-only benchmark reporting with a cost-side audit of chain-of-thought —the same discipline the 10/27 runtime ledger applied to context.

Evidence: 866 Python tasks (HumanEval Pro, MBPP Pro, BugsInPy) across five model settings. Qwen3-32B with reasoning on: HumanEval Pro pass@1 71.1% → 76.5%, BugsInPy tests passed 3.5% → 7.9% – but MBPP Pro moves 68.1% → 69.5%, not statistically significant, while burning a median of over 2,000 thinking tokens per task. Across models, longer median traces do not mean higher accuracy. Truncation experiments: retaining the first half of a stored trace yields no significant correctness drop, and no adaptive stop rule (repetition detection, first-code-appearance) beats plain fixed truncation.

Trade-offs: Reasoning is worth it where fault localization is the bottleneck (bug fixing) and mostly burns tokens where implementation choice is the bottleneck. No adaptive truncation rule was found — the boring fixed cut wins again.

Verdict: Report reasoning cost beside correctness; budget thinking like any other resource. Half the trace for half the cost is the default policy the data supports.

3. AgentBaton: Efficient Model Collaboration in Agentic Tasks arXiv:2610.35959

Claim: Within a single long-horizon agentic task, capable and efficient models can collaborate through handoffs to cut cost while preserving accuracy.

Foundation: Replaces per-query routing (one model per request) and multi-model debate with a one-time, well-timed handoff inside the task.

Evidence: 11 model pairs, two benchmarks. The asymmetric core finding: efficient-to-capable handoffs generally UNDERPERFORM capable-only execution with limited or no cost reduction, while capable-to-efficient handoffs at suitable task-progress stages largely preserve or even improve accuracy (up to +8.8 points) at 65.9% lower API cost on average. AgentBaton is a lightweight detector that learns when to hand off from recurring task-progress patterns. The abstract tail (reasoning-effort experiments) truncates mid-sentence on arxiv – the core numbers above are complete.

Trade-offs: One detector, one handoff — refreshingly cheap compared to orchestrators. But direction is everything: the naive "escalate when unsure" shape (cheap-first) is exactly the one that loses.

Verdict: The cheapest collaboration primitive in the batch: one well-timed handoff, capable-to-efficient, gated on task progress. Deployable without any new infrastructure.

4. CASEL: Consequence-Aware Selective Execution for Reliable Long-Horizon Tool-Using Agents arXiv:2610.36570

Claim: Runtime safeguards should allocate scrutiny per tool call by consequence, reversibility and evidence freshness – not generic uncertainty scores.

Foundation: Extends the 10/16 score-gating critique into a full per-call allocation policy over an intervention menu: execute, verify, sandbox, clarify, approve, abstain.

Evidence: Controlled simulation, 20 seeds x 120,000 test actions, every method given the same intervention menu and same compute budget: CASEL cuts harmful-side-effect trajectories from 14.75% (uncertainty-ranked gate) to 7.02% and mean normalized harm from 0.184 to 0.123. A probability-times-consequence gate reaches similar harm (5.82%, 0.120) but sends 45% more actions to human approval; removing reversibility-aware routing raises it to 15.53%. A trajectory-level risk budget INCREASES harm at equal cost (11.82%).

Trade-offs: The authors flag the load-bearing assumption themselves: intervention effectiveness is assumed, not measured — results rank allocation rules under the simulator's model of what each intervention does, and do not establish deployment effectiveness.

Verdict: Adopt the policy shape (consequence and reversibility over raw uncertainty; per-call over trajectory budgets), not the numbers. This is the fourth straight week where allocation-by-consequence beats allocation-by-score.

5. CIVER: Causal Intervention Verification for Reliable Code Review arXiv:2610.36010

Claim: LLM code-review findings can be separated from false positives by intervention: perturb the code toward a repair and check whether the finding responds.

Foundation: Replaces single-pass LLM review and pure judge ensembles with a falsification test — a review claim must survive a targeted perturbation to count as a defect.

Evidence: SWE-Review-Bench: CIVER (review + intervention + verifier modules, jointly optimized via expert iteration) correctly identifies 77% of genuine defects through targeted repairs, while remaining stable under unrelated changes. Cross-project and downstream repair evaluations show generalization and improved coding-agent task success rates.

Trade-offs: Each verified finding costs perturbation tokens. The method is tested on review findings, not raw agent output; review-volume sensitivity is not stated.

Verdict: A reviewer that must survive its own proposed fix is a mechanized version of last week's challenge-procedure insight – cheap, replayable, and directly implementable on top of any existing review agent.

6. God Does Not Play Dice: Enforcing Batch Invariance in Agentic Kernel Generation arXiv:2610.36623

Claim: Agentic GPU-kernel generation must enforce batch invariance – bitwise-identical outputs regardless of what else shares the batch – or serving reproducibility breaks.

Foundation: Pairs with the 10/1 Vosti result (batch-invariant vLLM differs from deterministic SGLang under ordinary execution variation) and moves enforcement from post-hoc audit to acceptance gates inside the agent's optimization loop.

Evidence: 19 confirmed batch-invariance violations found among 83 batch-applicable released kernel artifacts. Contract-guided static dependency analysis plus dynamic validation of bitwise consistency across batch contexts, integrated as successive acceptance gates that reject risky or non-invariant candidates before benchmarking and return feedback: BI pass rate rises from 67.6% to 91.9% while retaining 91.8% of the baseline geometric-mean speedup, across three agent frameworks.

Trade-offs: Gates reject candidates, which is wasted agent effort — but the feedback loop recycles them, and the gate cost is not itemized separately. Validated on real serving tasks, not toy kernels.

Verdict: Reproducibility as a first-class objective in agentic codegen, priced at ~8% of retained speedup. If you generate kernels with agents, this gate is cheap insurance.

7. Silent and Costly: Characterizing and Detecting Prompt-Caching Mis-Integration in LLM Agents arXiv:2610.34817

Claim: Prompt-caching mis-integration in agent systems is common and, crucially, SILENT – no runtime failure, just money.

Foundation: Replaces cache-hit-rate dashboard folklore with a static checker; a silent-misconfiguration class in the same family as 10/26's warped tool formatters.

Evidence: First empirical study: 120 prompt-caching-related PRs across 10 popular open-source agent systems. Two root classes: request-side misconfiguration and read-side usage miscount; most fail silently with no observable runtime error. CACHELINT locates invocation sites with CodeQL and discharges each precondition against a dated provider capability oracle or an LLM guard check: 0.71 precision / 0.78 recall on the labeled set; 32 mis-integrations found across 571 agent projects at 0.86 precision; 21 developer confirmations and 18 upstream fixes – including one in Anthropic's official SDK.

Trade-offs: Needs a dated capability oracle per provider — maintenance burden moves from runtime monitoring to lint upkeep. Coverage of provider API drift is inherently a treadmill.

Verdict: Highest practical-value item of the batch: a named, silent, money-leaking defect class with a working checker and real upstream fixes already landed. Run an equivalent check on your own agent stack today.

8. The Agent in the Middle: How AI-Mediated Access Reshapes Web Vulnerability Exploitation arXiv:2610.35541

Claim: Agent-mediated access to web services reshapes vulnerability exploitation – and exploitability is set by the interface, not the agent.

Foundation: Replaces the "agents make attacks easier" narrative with measured access-control analysis; the security installment of 10/26's interface audit.

Evidence: VELA benchmark: 44 vulnerabilities across 12 web application families, three commercial agents, independent oracles verifying attack outcomes. The agent realizes every attack that direct interface invocation realizes; 91% of vulnerabilities remain exploitable under the reference design. Interface design moves exploit-retention from 39% to 95%, while agent choice moves it at most 4.5 points. No case-level loss is attributed to agent refusal or input normalization – and in one case the agent completed an attack by supplying calls the attacker omitted.

Trade-offs: Restrictive interfaces that remove required operations also limit legitimate workflows — the paper measures both sides instead of asserting a trade-off.

Verdict: Security budgets should buy interface restrictions before agent-side filters. The agent is a passthrough, not a boundary; harden the API.

9. kw-bench: Breaking Frontier Harnesses on Real-World Tasks arXiv:2610.36405

Claim: Frontier coding-agent harnesses break on investigative knowledge work — audit, tax, due diligence – not on generative work.

Foundation: Complements the generative-harness benchmark wave (financial models, memos, rubric-judge builds) with the expensive half of knowledge work: recovering and defending an answer that already exists in inconsistent evidence.

Evidence: First task family: a five-year VAT reconstruction across three generations of SAP (legacy ECC, partial S/4HANA migration, a consultant's redesign) delivered as raw system extracts. Deterministic grading, no LLM judge. Four harnesses on the same model (DeepSeek v4.1 Flash) for two hours: the two model-agnostic harnesses delivered (OpenCode 0.542, micro-cc 0.528) while the two lab harnesses, away from their home model, delivered 0.00 each – defeated by inertia, keeping many weak signals alive across a long session. On Claude Opus 5.5 the task collapses: micro-cc 0.9986 and Claude Code 1.000 in 22 and 25 minutes.

Trade-offs: Single-author, one task family so far, and the 0.00-versus-0.54 split is harness-model coupling, consistent with the 10/8 concurrency audit's finding that model capability dominates long-horizon work.

Verdict: Harness lock-in is now measurable: away from home model, two flagship lab harnesses deliver literally nothing. Benchmark worth watching as more task families land.

10. How Stable Are LLM-Generated Proofs Under Program Refactoring? A Stress Test for Verifiable Code Generation arXiv:2610.36328

Claim: Verifiable-codegen benchmarks test a proof against one fixed implementation; the question is whether the same proof survives a behavior-preserving refactoring.

Foundation: Stress-tests the Verina-lineage proof benchmark design and extends the determinism arc (Vosti, IRONPROOF) from inference to proof artifacts.

Evidence: 458 Lean-certified variants derived from the 185 VERINA tasks that compile in a pinned environment, specification and evaluated proof held byte-identical. Structural proof-stability rate: 4.3% of refactorings break the supplied reference proofs; 13.3–26.8% break specialized-prover proofs – BUT on the matched task subset, prover proofs have HIGHER overall unchanged-proof survival than the human-authored references: +15.6 points for DeepSeek-Prover-V2-7B, +18.8 for Goedel-Prover-V2-32B, +20.3 for Goedel-Prover-V2-8B, all 95% CIs excluding zero. And brittleness is cheap to fix: predefined one-identifier edits repair 51/54 eligible reference failures and 58/85 eligible prover failures.

Trade-offs: Measures unchanged-proof stability, not repair cost at scale; the matched model-versus-reference comparison rests on 21–25 short, non-recursive tasks.

Verdict: Proofs are brittle but cheaply repairable — brittleness is not the bottleneck. The quietly interesting reversal: machine-generated proofs survive refactors better than the human-authored reference proofs.

11. Beyond QASecClaw: Agent Scaling, Verifier Feedback, and Evidence Reliability in Software Assurance arXiv:2610.36947

Claim: Additional specialist LLM contexts improve software assurance when deterministic tool evidence is already available.

Foundation: Tests the stacking assumption – more contexts, more judges, more feedback – that underlies most assurance-agent designs.

Evidence: Five experiments, 13,638 model requests, Qwen2.5-Coder-7B (1.5B sensitivity arm). On eight related development warnings, raising context count from 1 to 50 increases token use 49-fold with NO improvement in unsafe clearance. On 32 SV-COMP tasks, raw verifier feedback raises correct model verdicts from 12.7 to 22.0 but also raises incorrect verdicts from 1 to 7 – while a JBMC-plus-JVM validation baseline returns 26 correct verdicts. On 240 deliberately misleading evidence packages, models reject 70–100% of applicable counterexamples while construction-specific scope checks classify every package correctly.

Trade-offs: Small-model results (7B class) may not transfer to frontier models; the authors label the study descriptive, and the Defects4J arm found no target defects (one unsupported by the test adapter).

Verdict: The fourth confirmation this month that stacking LLM context on deterministic evidence dilutes rather than sharpens. Deterministic evidence admission is the winning default; LLM context is the expensive garnish.

12. Skill Dreaming: Evolving Repository Skills through Pull Request Streams arXiv:2610.36188

Claim: Skill libraries should co-evolve with pull-request streams: validated PRs become executable skills, and the skills improve future PRs.

Foundation: Extends the skills-ecosystem arc (10/10 supply chain: copy networks, conflicting skills, PyCache trap) from auditing what exists to a mechanism for growing skills.

Evidence: Framework only: PR analysis + skill management + code execution + CI, forming an end-to-end workflow where skills guide feature implementation plus tests, documentation, compatibility and conventions, and subsequent PR outcomes feed back into skill updates. No benchmark numbers in the abstract.

Trade-offs: The validation gate is unspecified (which PRs count as validated?), and the mutual-reinforcement loop risks compounding stale knowledge — precisely the failure the 10/10 copy-network study measured (copies almost never updated from source).

Verdict: Pattern, not evidence — flagged. The interesting open question is the admission gate: a skills library is only as good as its rejection rule.

13. Rare, Relevant, and Still Wrong: How Memory Fails LLM Group Decisions arXiv:2610.37624

Claim: When an LLM group's discussion is compressed into memory for a final decision-maker, rarity- and relevance-based selection heuristics both fail in predictable places.

Foundation: Replaces memory-selection heuristics in multi-agent summarization; the clean causal companion to 10/26 Compact-Bench and 10/22 Conflicted Records.

Evidence: CrossProfile: 60 templates in which ownership of supporting, decoy and irrelevant facts is crossed while text is held fixed – 480 tasks with automatically verified answer keys, across four models. A perfect relevance filter LOSES 23–31 points to rarest-first selection in the classic hidden-profile configuration because decoys are on-topic. Rarest-first is no better than random on average. The decision model adds a third failure: social proof – extra weight to facts attributed to many agents; removing or randomly reassigning attribution tags raises accuracy 13–24 points. In natural discussions the evidence is lost before memory acts: the complete transcript yields LOWER accuracy than no notes at all.

Trade-offs: Synthetic templates with controlled crossing – external validity is open. But the crossing is exactly what makes the attribution-tag finding causal rather than correlational.

Verdict: The strongest clean causal result of the batch, and trivially actionable: strip attribution tags from compressed group memory before handing it to a decision-maker.

14. INC-MoE: Overlapping In-Network Collectives and Expert Computation for Expert-Parallel MoE Inference arXiv:2610.36339

Claim: Offload MoE dispatch and combine to the network switch (in-network AllGather via multicast, ReduceScatter via reduction) and overlap both with expert computation.

Foundation: Replaces ring-based collectives on the MoE critical path; infrastructure sibling to 10/8–era host-centric MoE routing work.

Evidence: In-network collectives load opposite directions of each link, cutting bandwidth-bound communication time by (N–2)/(2(N–1)) – 33% at four ranks, 43% at eight. A single persistent kernel runs a three-stage AllGather-FFN-ReduceScatter pipeline with role-specialized thread blocks. Drop-in replacement for vLLM's MoE layer: on OLMoE prefill with four-way expert parallelism, TTFT down up to 33% and throughput up up to 50% versus the default collective path, outputs matched. Caveat disclosed: evaluation is an EMULATED four-node deployment with a dedicated accelerator standing in for the INC switch.

Trade-offs: Requires in-network-computing-capable switch hardware; the emulation gap is stated but not quantified. Real-switch silicon results are the missing evidence.

Verdict: Coherent architecture, honest emulation caveat. Watch for the real-switch replication before provisioning decisions.

15. Merkle-Tree-Based Verifiable Public Data Auditing for Cloud Object Storage Using Smart Contracts arXiv:2610.36001

Claim: Cloud object-storage owners can verify provider integrity against a Merkle root anchored in an Ethereum smart contract, without third-party auditors or full downloads.

Foundation: Replaces the traditional trusted-auditor model and the full-download compare-everything audit.

Evidence: SHA-256 of each object forms a Merkle leaf; one root anchored on-chain. Five experiments on an S3-compatible store with a local Hardhat chain: 15/15 single modifications, 2,205/2,205 modified objects and 830/830 deletions detected with no false alarms. A 10,000-object proof needs 14 hashes; one object checks in ~10 ms, a 100-object sample in ~150 ms regardless of dataset size; full audit of 10,000 objects took 14.3 s versus 13.5 s to download them. Root storage: 91,863 gas — 252x cheaper than storing 1,000 hashes on-chain.

Trade-offs: Local testnet only — no mainnet gas/reorg analysis. And the trust question is relocated rather than eliminated: whoever writes the root to the contract is the new trusted party.

Verdict: Sound idea, textbook execution, trust question honestly relocated from auditor to root-writer — still unresolved, but at least stated.

16. Taming Multi-Dimensional Skew in Sparse Matrix Multiplication with Contention-Aware Scheduling arXiv:2610.36822

Claim: SpGEMM performance loss under real-world skew comes from two co-existing root causes – multi-dimensional imbalance (compute cost, output density, locality) that no single metric captures, plus cache contention from dense substructures.

Foundation: Replaces single-metric load balancing with decoupled skew handling: adaptive morsel scheduling with fixed-result-size morsels, contention-aware balancing, data-conscious SMT activation and hybrid accumulation.

Evidence: NUS group (He, Tan). AMS significantly outperforms state-of-the-art methods on skewed workloads while maintaining comparable performance on non-skewed ones, across matrix squaring, relational queries, graph analytics and other sparse kernels, with gains and improved bandwidth utilization persisting across a wide range of workloads and hardware.

Trade-offs: The abstract gives directional claims only – "significantly outperforms" with no headline number; morsel-sizing overhead unstated. Need the paper tables before quoting magnitudes.

Verdict: Credible systems work from a strong group; the transferable idea is the two-root-cause decomposition (skew dimensions decoupled from contention), not any quoted speedup.

17. Generation and Verification: Human-Agent Collaboration for Safety-Critical Automotive Software Engineering arXiv:2610.35982

Claim: Safety-critical automotive software engineering needs verification the developer can invoke on demand – not agent self-checking.

Foundation: The industrial echo of the month's arc: generation and verification are different authorities. MCP-exposed generate / edit / verify tools; AUMOVIO production code; MISRA and AUTOSAR conformance checking.

Evidence: Two-week pilot on active production projects: verify was the most-used tool, and developers confirmed using it on their OWN code, not only on generated output — the strongest possible vote for verification-as-tool over agent self-assessment. Model customization comparison: 75% of developers preferred the faster prompt-engineered configuration over the fine-tuned one; MISRA/AUTOSAR-inspired pattern checkers show prompt engineering achieving fewer violations on C, fine-tuning better conformance with architecture patterns on C++.

Trade-offs: Pilot scale with developer self-report; domain-customized to automotive C/C++ – generalization beyond the domain unstated.

Verdict: Deployers are voting with usage: they want a verify button, not an agent that promises it checked itself. Third independent confirmation of the separation-of-authorities theme this month.

Radar — Watch, Do Not Quote

2610.37342 Aligning Institutional Power and Operational Ability – formal framework for norm-ability alignment: matching responsibilities to the ability to fulfil them, repairing unsatisfiable expectations and privilege escalation. Theory only, no benchmarks – but it is the governance vocabulary the 10/21 contract layer was reaching for.
2610.35978 Testing Agentic Service Systems (SOC4IA @ ICSOC 2026 workshop, peer-reviewed venue) – an agentic living-lab methodology across the physical-digital continuum; 46 pages. Methodology paper, not a benchmark.
2610.37224 Spine – compiles subtask trees into structured context for long-horizon coding agents; competes directly with the whole-file-context result from 10/16 (Skip the Compressor).
2610.36584 – benchmarking LLM agents for behaviorally adequate test generation from software requirements – behavioral adequacy, not pass/fail, as the grading axis.
2610.37343 HIDE – hierarchical isolation via documentation and evidence for rustc bugs; the documentation-as-evidence pattern applied to compiler faults.
2610.36894 ASNER – software architecture recovery with adaptive semantic neighborhoods and evidence-grounded refinement.
2610.36300 Building Mathematical Software — Lessons from GAP – a 60-year survival analysis of a mathematical software system; the longest-horizon data point in the batch.
2610.33647 – speeding up log-sum-exp: kernel fusion at the memory wall, integer arithmetic at the compute wall – two-wall diagnosis worth stealing for any kernel budget.
2610.34914 Waypoint – analytically modeling GEMM for CPUs; performance models over autotuning search.
2610.37375 Agent-Conditioned Routing – diverse reasoning paths within one frozen model; routing inside a single model instead of across model pairs.
2610.36976 – circuit-aware mutation testing for quantum programs, with exact visibility and execution planning.
2610.23284 Annotrace – a workbench for step-level annotation of agent traces; the tooling the 10/28 instruction-stack audits needed.
FLAGGED: 2610.36254 Instant Software Factories – title reads as vision/position; verify scope before citing. 2610.35980 UnityBench-Mini3D and 2610.37649 (LLMs uncovering hidden physical laws through free exploration) are single-team benchmarks worth a second look next week.

Week Arc

September 26 verification as control plane → October 17 the weekend ledger → October 20 learner on probation → October 21 the contract layer → October 22 the assurance plane → October 23 the evaluation scarcity → October 26 the interface audit → October 27 the runtime ledger → October 28 the instruction stack → October 29 THE INTERVENTION LEDGER: every intervention – restart, longer thinking, more contexts, memory heuristics, verification gates, handoffs, interface restrictions – must price itself against the cheapest null action. The arc–wide synthesis is now precise: interventions pay when they carry structural information the model does not have (consequence, reversibility, interface boundaries, causal perturbation) and fail when they merely spend more of the same currency (tokens, contexts, votes). The three working interventions in this batch all move information, not effort.

Sources

All items read as submitted abstracts on arxiv.org, Wednesday, October 28, 2026 batch (remainder after the October 28 report): 2610.35971, 2610.36952, 2610.36405, 2610.36570, 2610.36623, 2610.34817, 2610.35541, 2610.36188, 2610.36010, 2610.36328, 2610.36947, 2610.36339, 2610.36001, 2610.36822, 2610.37624, 2610.35982 – radar: 2610.37342, 2610.35978, 2610.37224, 2610.36584, 2610.37343, 2610.36894, 2610.36300, 2610.33647, 2610.34914, 2610.37375, 2610.36976, 2610.23284, 2610.36254, 2610.35980, 2610.37649. The Thursday, October 29 announcement had not landed at publish time; expect fresh submissions in the next report. Prior coverage referenced: October 28 (instruction stack), October 26 (interface audit), October 22 (assurance plane), October 16 (coordination budget), October 10 (skills supply chain).