Certifying Plans under Model Mismatch: A Trilemma for Reachability from Scarce Data

• URL: http://arxiv.org/abs/2608.02453v1
• Date: Mon, 03 Aug 2026 16:28:26 GMT
• Title: Certifying Plans under Model Mismatch: A Trilemma for Reachability from Scarce Data
• Abstract summary: Sim-to-real policies are designed under nominal dynamics, but target-system trials may yield only a few isolated one-step transitions.<n>We study pre-execution certification of a fixed control sequence, such as an action chunk produced by a learned policy.
• Score: 5.455772242709494
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sim-to-real policies are designed under nominal dynamics, but target-system trials may yield only a few isolated one-step transitions. We study pre-execution certification of a fixed control sequence, such as an action chunk produced by a learned policy. If the sequence reaches an unobserved state-input region, the observations remain consistent with target systems whose trajectories separate along it by an arbitrarily large amount. Any deterministic certifier sound for all of them must then decline to certify or return a reachable tube with arbitrarily large projected width. For bounded smooth classes of the target-nominal model error, we derive a finite plan-dependent projected-width lower bound. These results expose a trilemma among uniform trajectory containment, finite projected width, and unrestricted model-error behavior beyond the observations. ForeReach requires a supplied componentwise Lipschitz bound on the model error. Observed transition pairs can refute this declaration but cannot establish it outside the observed locations. Conditional on a valid declaration, our method constructs a set-membership envelope for the model error, propagates a zonotopic reachable tube, and certifies only when propagation remains within the certification domain and every projected tube slice avoids the unsafe set. In two benchmark systems, calibration baselines may remain narrow after losing trajectory containment outside data support, whereas our method declines to certify unsupported sequences and recovers certification when relevant target data and sufficient obstacle clearance are available.

Related papers

• A distribution-free certification framework for trustworthy crash-severity prediction [1.089614199781423]
  We develop a certification layer that wraps any severity model unmodified.<n>On 5.2 million Texas records across seven base models spanning four decades, the layer attaches identical validity.<n>The framework is released as an open-source package with theorem-level tests.
  arXiv  Detail & Related papers  (2026-09-10T14:20:08Z)
• An Omitted Mode Is a Rare Rule: The Sampling-Verification Danger Law in Continuous Code World Models [0.0]
  GPT-5.x repairs an omitted 1D clamp in 105 of 111 mode-containing synthesis draws.<n>On 2D regions no artifact recovers the rule (0/156); eight targeted interventions leave the failure in place, and positive controls locate it.<n>We prove a class of entry rules exactly consistent with every sample yet harmless at play, so identifiability is a measurable property of the instrument.
  arXiv  Detail & Related papers  (2026-08-18T16:09:51Z)
• Post-Hoc Trajectory-Risk Certification for Modular LLM-Based Security Agents [1.4915002678801434]
  We show that Bonferroni allocation is distribution-free but conservative under correlated errors.<n> coarse-to-fine label selection can create near-perfect measured correlation without learned dependence.<n>A modular certificate using per-stage certificates and a pairwise overlap bound yields a positive average gain of 0.6%.
  arXiv  Detail & Related papers  (2026-08-04T23:48:36Z)
• What Can Be Enforced? A Theory of Certified Runtime Safety for Tool-Using Agents [0.0]
  guardrails act before irreversible tool calls, but their guarantees depend on what policy state is representable.<n>Pearson representation attacks add a margin, so benign calibration alone does not transfer.<n>Experiments target these distinctions through static diagnostics, controlled-model enumeration, representation rewrites, and paired closed-loop reruns.
  arXiv  Detail & Related papers  (2026-07-24T19:14:26Z)
• DriftScope: Measuring The Hidden Effects of Diffusion Model Adaptation [50.584834483403675]
  Adapting pre-trained text-to-image diffusion models, whether to learn new visual concepts or erase unwanted ones, is routinely evaluated on its intended effects alone.<n>We demonstrate that adaptation systematically damages semantically unrelated concepts in ways that aggregate metrics structurally cannot surface.<n>We introduce DriftScope, a prompt-level diagnostic tool that takes any two model checkpoints and returns a ranked list of tokens whose visual concepts have shifted most between them.
  arXiv  Detail & Related papers  (2026-06-30T20:57:51Z)
• PAC-Bayesian Certificates for Quadratic Closed-Loop Control [0.0]
  PAC-Bayesian bounds provide finite-sample guarantees for data-dependent predictors.<n>Applying them to learning-based control is difficult because the natural objective is a quadratic trajectory cost.<n>We provide a set of PAC-Bayes-Chernoff certificates for posterior distributions over feasible closed-loop responses.
  arXiv  Detail & Related papers  (2026-06-26T17:24:21Z)
• What Accuracy and Gradient Cosine Miss: Evaluating Feedback Alignment via Scale Stability, Reference Validity, and Depth Utility [48.10132234701036]
  We propose a diagnostic evaluation protocol based on three checks -- scale stability, reference validity, and depth utility -- together with per-layer rather than aggregate cosine reporting.<n>Across multiple architectures and methods, our protocol identifies all failures with wide calibration margins.
  arXiv  Detail & Related papers  (2026-06-19T06:04:17Z)
• A Held-Out Transition-Pair Falsifier for Long-Horizon Non-Abelian State Tracking [0.5076419064097734]
  We introduce a held-out transition-pair falsifier for finite non-Abelian group tracking.<n>In a controlled $S_3 times S_3$ benchmark, a projected recurrent state model trained only on length-8 sequences produces error-free final-state predictions.
  arXiv  Detail & Related papers  (2026-06-05T13:26:41Z)
• Compiling Activation Steering into Weights via Null-Space Constraints for Stealthy Backdoors [48.881343993730844]
  Safety-aligned large language models (LLMs) are increasingly deployed in real-world pipelines.<n> adversaries can distribute backdoored checkpoints that behave normally under standard evaluation but jailbreak when a hidden trigger is present.<n>Recent post-hoc weight-editing methods offer an efficient approach to injecting such backdoors.
  arXiv  Detail & Related papers  (2026-04-14T06:48:33Z)
• How Far Can Unsupervised RLVR Scale LLM Training? [57.44753418846446]
  Unsupervised reinforcement learning with verifiable rewards (URLVR) offers a pathway to scale LLM training beyond the supervision bottleneck.<n>Recent works leverage model intrinsic signals, showing promising early gains, yet their potential and limitations remain unclear.<n>We classify URLVR methods into intrinsic versus external based on reward sources, then establish a unified theoretical framework.
  arXiv  Detail & Related papers  (2026-03-09T17:38:11Z)
• Conformal Tradeoffs: Guarantees Beyond Coverage [0.02648566468224904]
  Deployed conformal predictors are long-lived decision infrastructure operating over finite operational windows.<n> Marginal coverage does not determine deployment-facing quantities.<n>We provide a framework for operational certification and planning beyond coverage.
  arXiv  Detail & Related papers  (2026-02-20T07:58:25Z)
• LEC: Linear Expectation Constraints for False-Discovery Control in Selective Prediction and Routing Systems [95.35293543918762]
  Large language models (LLMs) often generate unreliable answers, while uncertainty methods fail to fully distinguish correct from incorrect predictions.<n>We address this issue through the lens of false discovery rate (FDR) control, ensuring that among all accepted predictions, the proportion of errors does not exceed a target risk level.<n>We propose LEC, which reinterprets selective prediction as a constrained decision problem by enforcing a Linear Expectation Constraint.
  arXiv  Detail & Related papers  (2025-12-01T11:27:09Z)
• Building a Foundational Guardrail for General Agentic Systems via Synthetic Data [76.18834864749606]
  LLM agents can plan multi-step tasks, intervening at the planning stage-before any action is executed-is often the safest way to prevent harm.<n>Existing guardrails mostly operate post-execution, which is difficult to scale and leaves little room for controllable supervision at the plan level.<n>We introduce AuraGen, a controllable engine that synthesizes benign trajectories, injects category-labeled risks with difficulty, and filters outputs via an automated reward model.
  arXiv  Detail & Related papers  (2025-10-10T18:42:32Z)
• Unsupervised Conformal Inference: Bootstrapping and Alignment to Control LLM Uncertainty [49.19257648205146]
  We propose an unsupervised conformal inference framework for generation.<n>Our gates achieve close-to-nominal coverage and provide tighter, more stable thresholds than split UCP.<n>The result is a label-free, API-compatible gate for test-time filtering.
  arXiv  Detail & Related papers  (2025-09-26T23:40:47Z)
• Verification-Guided Falsification for Safe RL via Explainable Abstraction and Risk-Aware Exploration [8.246285288584625]
  We propose a hybrid framework that integrates explainability, model checking, and risk-guided falsification to achieve both rigor and coverage.<n>Our approach begins by constructing a human-interpretable abstraction of the RL policy using Comprehensible Abstract Policy Summarization (CAPS)<n>If no violation is detected, we cannot conclude satisfaction due to potential limitation in the abstraction and coverage of the offline dataset.
  arXiv  Detail & Related papers  (2025-06-04T00:54:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.