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Agent MechSuits: Mechanistic Subspace Safety Steering for Multi-Turn CLI Agents
2026-11-12 01:46:14Weidi Luo, Qiming Zhang, Yihao Quan, Mingyu Jin, Jie Cai, Chaowei Xiao, Jingcheng Niu, Zhen XiangAbstract
Command-Line Interface (CLI) agents based on large language models (LLMs) demonstrate remarkable autonomous capabilities, but they also introduce significant safety and misuse risks during multi-turn interactions with external environments. Existing safety mechanisms mainly rely on external guardrails, which have a limited ability to perform fine-grained behavioral control during execution. Meanwhile, recent mechanistic interpretability methods for LLM safety are mostly confined to single-turn or jailbreak-style QA settings, limiting their ability to capture the evolving risk dynamics of multi-turn agent execution. In this paper, we investigate the safety of multi-turn CLI agents from an internal perspective. We propose Agent MechSuits (Mechanistic Subspace Intervention and Steering), a white-box defense framework that performs runtime safety detection and representation-level mitigation for CLI agents. Unlike conventional agent guardrails, Agent MechSuits detects harmful execution states from step-level hidden representations and mitigates unsafe behavior by intervening in a 10-dimensional subspace within a single layer. To support this research, we introduce the Mechanistic Agent Safety (MAS) benchmark, comprising comprehensively annotated multi-turn execution trajectories across 194 tasks using LLaMA-3.1-8B, Qwen-2.5-7B, and Gemma-2-9B. Extensive experiments show that Agent MechSuits achieves strong safety detection performance, provides preliminary evidence for lookahead risk anticipation, and substantially reduces harmful actions of the CLI agent, establishing a foundation for applying mechanistic interpretability to dynamic LLM agent safety.
Abstract (translated)
URL
https://arxiv.org/abs/2606.22673
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Program Learning with Verifiable Rewards: Distilling Expert Traces into Programs
2026-11-12 00:38:11Vishvesh Bhat, Muhammad Ahmed Mohsin, Asad AaliAbstract
Small language models struggle with tool-use tasks that require following multi-step procedures. Whether these failures reflect limited capability or missing procedural knowledge remains unclear. We introduce Program Learning with Verifiable Rewards (PLVR), which distils expert traces into reusable programs without updating model weights. Frontier models turn recurring operations in the traces into typed primitives and rules, and search assembles them using reference actions and training-task checks. A small frozen model executes the primitives, with deterministic verifiers checking their outputs. The resulting program guides a student's reasoning or checks its proposed actions. We evaluate the same program across students on $\tau^2$-Bench Retail and Telecom and LiveMCPBench, using synthetic training tasks and unseen benchmark test tasks. On Telecom, PLVR raises an 8B model's success rate from 43.0% to 91.2%, while larger students reach up to 94.4%, compared with 57.9% for the best baseline. On Retail and LiveMCPBench, PLVR is competitive with the best baselines, although tool search remains more dependent on the student model. These results support program learning as a way to transfer expert procedures across frozen models, trading additional inference computation for more reliable tool use.
Abstract (translated)
URL
https://arxiv.org/abs/2610.19148
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ADAPT: Agentic Diff-Aware Patch Testing for Pull Requests
2026-11-11 23:48:27Vahid Haratian, Atakan Akar, Eray T\"uz\"unAbstract
Modern software development relies on pull requests (PRs) that introduce small, targeted code changes. Once merged, these changes should be protected by focused tests that exercise the behavior the PR introduces, yet developers often submit PRs without such tests, leaving the new behavior unguarded against future regressions. Most test generation techniques target entire software units and optimize objectives such as code coverage rather than the behavior a change introduces, and prior diff-aware approaches have had limited success in capturing it. We propose ADAPT (Agentic Diff-Aware Patch Testing), a regression test generation approach that uses an autonomous coding agent to produce change-focused tests for PRs. Given a PR, the agent analyzes the diff between the base and head versions to identify the modified functions and their context, then plans and generates candidate unit tests. A dedicated fail-to-pass (F2P) verification tool runs each candidate on both versions and, after F2P success, reports which executable changed lines remain uncovered; guided by this feedback, the agent iteratively refines its tests. We evaluate ADAPT on the same 426 real-world PRs used by the prior state-of-the-art baseline, with GPT-4o and GPT-5-mini. ADAPT+GPT-4o reaches an F2P rate of 62.0% (264/426), compared with BLAST's reported 35.4% (151/426), while ADAPT+GPT-5-mini reaches 89.9% (383/426). On 137 PRs shared with Change And Cover (ChaCo), ADAPT achieves full patch coverage on 83.9% of PRs, compared with 79.6% for ChaCo, at considerably lower time and cost. A manual evaluation confirms that the F2P-valid tests target each PR's intended change, with mean scores of up to 4.54/5 for importance, 4.35/5 for assertion strength, and 4.49/5 for readability, often covering behavior beyond the developer-written tests.
Abstract (translated)
URL
https://arxiv.org/abs/2611.16114
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Subsurface schematisation from sparse CPT data using the tabular foundation model TabPFN
2026-11-11 22:57:25Taiga Saito, Fabian Campos Montero, Bruno Zuada Coelho, Ana Martins Teixeira, Stephen Wu, Timo Schweckendiek, Yu OtakeAbstract
Subsurface schematisation supports geotechnical decisions but remains challenging because sparse site-investigation data leave substantial gaps in information about ground conditions. This study explores TabPFN, a foundation model pretrained on general synthetic tabular data, for this task. Using only grid coordinates and soil behaviour type index observations from sparse cone penetration tests (CPTs), we first reconstruct two-dimensional sections without geotechnical fine-tuning or predefined layer boundaries. We evaluate the approach on synthetic sections and at two field sites in the Netherlands, comparing it with SchemaGAN, a conditional generative adversarial network trained on synthetic geotechnical sections, and with reference interpolation methods. In the baseline comparisons, TabPFN achieved mean errors close to SchemaGAN's on synthetic sections and lower mean errors at both field sites. On synthetic sections, TabPFN's mean absolute error decreased at each tested increase in CPT count, and, under the baseline input setting, its pointwise prediction intervals had mean coverage close to nominal levels. Subsequent fine-tuning on the synthetic sections used to train SchemaGAN improved synthetic test accuracy but increased mean errors at both field sites. These findings indicate that general-purpose tabular foundation models offer a promising route for updating subsurface predictions from sparse and evolving site-investigation data without retraining.
Abstract (translated)
URL
https://arxiv.org/abs/2611.16088
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Agentic Computational Algebra
2026-11-11 22:32:22Ryan A. Anderson, Matthew Trager, Ben Bowman, Wei Xia, Stefano SoattoAbstract
We explore the use of AI agents for solving computational problems in algebra. For the problems we consider, the main challenge is not correctness but \emph{time}: general-purpose algorithms are guaranteed to provide correct solutions but can be prohibitively slow, while reformulations that exploit special structure often allow solving a problem much faster. We propose to use this setting to evaluate how effectively agents discover and exploit structure within and across tasks to reduce computational costs. More broadly, computational algebra supplies infinitely many problems with independently verifiable answers and systematically controllable structure and difficulty. To illustrate these possibilities, we use benchmark families of systems with controlled shared structure and evaluate agents in three settings: solving independent instances, solving online streams of related instances using persistent memory, and compiling a fast reusable solver for a known problem family. Our experiments show that (i) agents can extend the range of systems solvable within a fixed budget, although reasoning overhead can sometimes outweigh computational savings; (ii) memory can reduce the cost of repeated solving but may preserve suboptimal strategies; (iii) compilation produces faster reusable solvers but at substantial upfront cost. Overall, computational algebra offers concrete, measurable targets for improving how agents discover and reuse structure.
Abstract (translated)
URL
https://arxiv.org/abs/2611.16073
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Pyramidal Columns Acting as Attention Heads
2026-11-11 22:13:12Fabrizia Giulia Garavaglia, Marco GiuntiAbstract
Can the functional architecture of a Transformer attention head be given a biological construction? More specifically, could a suitable group of pyramidal columns in posterior temporal language regions (PTLRs) constitute the biological construction of a neuromorphic mechanical realization of an attention head, under a mathematical instantiation that need not coincide with its standard Transformer instantiation? This paper investigates this hypothesis by examining possible correspondences between the functional organization of attention heads and neurophysiological mechanisms operating within and across pyramidal columns involved in human language processing. Our analysis builds on a mechanical-structural description of Transformer attention heads as systems of simple neural networks linked through output-input and output-weight interconnections. We examine the functional organization of an attention head and the input-dependent transformations it produces, and ask how the corresponding processing relations could be constructed in biological neural tissue. We then identify candidate neural mechanisms of signal transmission and modulation within and across pyramidal columns that may jointly support such a construction. On this basis, we propose that a suitably organized group of pyramidal columns in PTLRs could constitute a biological construction of a neuromorphic mechanical realization of an attention head. Whether the required functional organization actually exists in PTLRs remains an open empirical question. The hypothesis does not require the biological system to reproduce the standard mathematical instantiation of Transformer attention: the same functional architecture may be mechanically realized and mathematically instantiated in a different way, consistent with its biological construction.
Abstract (translated)
URL
https://arxiv.org/abs/2611.16057
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Fit-LRP: Fitting Linear Relaxations to Polytopes for Neural Network Verification over Non-Convex Regions
2026-11-11 21:05:13Brian Yarbrough, Jesse Wayment, Jingbo Wang, Philip E. Par\'{e}, Shreyas SundaramAbstract
State-of-the-art neural network verifiers evaluate linear relaxations over axis-aligned bounding boxes. However, verification domains in cyber-physical systems are rarely boxes. Reachability specifications, obstacle-avoidance constraints, and neural Lyapunov certificates typically yield oblique or non-convex polyhedral regions, over which box-based bounds become extremely loose. This paper proposes Fit-LRP (Fitting Linear Relaxations to Polytopes), a framework the implements exact optimization of affine bounds over non-convex domains (represented by unions of polytopes) by solving a sequence of small, low-dimensional linear programs. Our approach seamlessly enhances the final optimization step of LiRPA and $\alpha,\! \beta$-CROWN branch-and-bound (BaB) algorithms. In BaB verification, approximating oblique constraints with axis-aligned splits causes severe combinatorial blowup and risks non-termination. By optimizing the final affine bound of a nonlinear function over the exact polyhedral state-space constraints our method eliminates box-induced over-approximations, enabling earlier search-tree pruning. On ARCH-COMP and neural Lyapunov benchmarks, Fit-LRP reduces branching on the hardest region by $47\times$, cuts worst-case verification time by $15\times$, and with region-wise partitioning, verifies on a single CPU $6.1\times$ faster than $\alpha,\! \beta$-CROWN on a GPU.
Abstract (translated)
URL
https://arxiv.org/abs/2611.16003
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Spec-Driven PBT: Deriving Semantic Invariants from Code Specifications for Agentic Property-based Testing
2026-11-11 19:16:05Aditya Shukla, Kartik Gupta, Ali Alavi, Pashootan VaezipoorAbstract
Software is increasingly written by AI agents that produce an implementation from a natural language specification, a practice known as agentic coding. Code is now produced faster than it can be reviewed, and has been shown to exhibit its own characteristic failure patterns. Property-based testing (PBT), a long-standing technique in which invariant properties of a program are checked against many generated inputs, has recently been revisited with LLM agents authoring the properties, thereby removing the high-effort derivation of invariants that has long hindered its adoption. Existing methods, however, derive these properties from the implementation alone (Code-driven PBT). At the same time, a thorough specification such as a product requirements document has become routine within agentic coding, yet its role in testing the resulting code remains underexplored. We study the value of the specification as a source of properties for testing agent-written code. Our key insight is that the specification, unlike the implementation, records what the code was meant to do, and invariants derived from it are not biased by particular implementation choices. We propose Spec-Driven PBT, an agent that first expresses invariants in natural language from the specification and only then inspects the implementation to translate them into concrete tests. Across 25 applications from ViBench, Spec-Driven PBT finds a novel set of bugs that Code-driven PBT misses, while writing 2.4x fewer property tests. Though the two approaches surface a roughly equivalent number of bugs, there is little overlap: nearly 60% of the 169 total bugs are found by only one. Qualitatively, we find that the bugs unique to Spec-Driven PBT tend to violate documented product workflows whereas those to Code-driven PBT instead concentrate on implementation robustness, offering complementary value for bug discovery in agent-written code.
Abstract (translated)
URL
https://arxiv.org/abs/2611.15920
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Before AI Agents Negotiate for Us: A Competitive Tournament of Rule-Based, Search, Optimization, Learned, and Language-Model Negotiators Under Scarcity
2026-11-11 18:48:20Eduardo de Avila-Armenta, Ra\'ul Alejandro Aquino-Ibarra, Jorge Octavio Nicol\'as-D\'iaz, Umberto Leon-DominguezAbstract
Automated bilateral negotiation underlies two-sided matching markets such as freight brokering, where many shippers and carriers negotiate price before a matching mechanism decides who is paired with whom. Prior strategy comparisons typically use isolated one-on-one duels or a single fixed market, so they cannot show how a strategy behaves against several live rivals under a changing balance of supply and demand. We built nine negotiation architectures spanning rule-based, search-based, optimization-based, imitation-learning, and reinforcement-learning families, and ran all nine simultaneously on both sides of a Gale-Shapley matching market under a balanced condition and two scarcity conditions rotated across all 36 exclusion pairs. In the balanced carrier market, an imitation-learned engine (ML2), a reinforcement-learning policy trained only on isolated negotiations (RL), and an expected-utility optimization engine (OPT) formed a statistically tied top group, and no architecture led in every condition and role. RL beat all eight carrier rivals when carriers were scarce, but its match rate collapsed from 62% to 16% when shippers were scarce and it had to compete with the other carrier agents for demand. A second reinforcement-learning policy trained against rival agents (RL2) kept its match rate above 91% in every condition, yet lost on realized payoff to all eight shipper-side rivals in two of the three conditions, traced to a confirmed train/deployment feature mismatch. We also tested nine large language models (LLMs), from 1 to 72 billion parameters, under the same protocol: they lost significantly to the two strongest engines (OPT2 and ML2) in 32 of 36 comparisons and won none. These results show that isolated or single-condition evaluation can hide failure modes that a many-sided, scarcity-varying, statistically corrected tournament exposes directly.
Abstract (translated)
URL
https://arxiv.org/abs/2611.15838
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Characterizing Scanpath Decoding Pipelines with Perturbation Profiles
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When a scanpath descriptor supports a classification, it is rarely clear what the classification rests on. We propose perturbation profiles. Five transformations each target one stated property of every scanpath (fixation order, the coupling of durations to positions, the duration distribution, orientation and handedness, and fine spatial structure) while preserving a listed set of invariants. The classifier is refit, and the change in participant-held-out AUC is the pipeline's sensitivity to that property. Intervals come from a participant bootstrap that refits the classifier; in simulations they were conservative (99.7% coverage; standard errors about 1.8 times the empirical spread). We profiled nine descriptors on thirteen binary problems from three public datasets, with a within-fold AUC and class-balanced folds and a stated rule for which sensitivities to interpret. Of 565 intervals, 95 excluded zero; 80 were robust to the pooled AUC, 20 fold partitions and longer runs (36 of them within +/-0.05 AUC), and 45 also held with boosted trees. Most came from GazeBase (322 participants). Permuting fixation order raised the AUC of a descriptor based on mean saccade amplitude, because permuted amplitude has as its expectation the mean pairwise distance, a measure of spread. A "comprehension" contrast pooled over two programs was a contrast between their layouts: it vanished among trials of the same program, and synthetic gaze drawn from the layout alone reproduced the program contrast beneath it. For native versus non-native readers, spatial jitter at the scale of text lines reduced recurrence's discrimination. A Python package is released.
Abstract (translated)
URL
https://arxiv.org/abs/2611.11632
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Fine-Grained Generative Hallucination Detection and Correction for Aerial Image Captioning
2026-11-11 18:26:26Debolena Basak, Somsubhra De, Maunendra Sankar DesarkarAbstract
Aerial imagery captured by unmanned aerial vehicles (UAVs) plays a vital role in applications such as surveillance, disaster response, and large-scale environmental monitoring. Automatic generation of textual descriptions for such imagery is essential for reducing manual inspection overhead and enabling better downstream decision-making. However, Multimodal Large Language Models (MLLMs), despite their impressive captioning capabilities, are prone to hallucination, producing descriptions that are inconsistent with the visual input. This problem is significantly amplified in aerial imagery due to its unique challenges such as extreme viewpoint variation, small objects, class imbalance, and ambiguous visual entities. Existing hallucination detection approaches either rely on computationally expensive large external models such as GPT-4, which perform poorly on aerial domains, or treat the problem as a rigid binary classification task, ignoring the inherent uncertainty in ambiguous cases. To address these limitations, we propose a lightweight, reference-free framework for hallucination detection and correction in free-form aerial image captioning. Our method leverages state-of-the-art MLLMs, specifically InternVL3 and Qwen2.5-VL, to detect hallucinated content in generated captions with minimal computational overhead. Detected hallucinations are corrected through a dedicated refinement module that improves the factual consistency of the original captions. Extensive experiments on the Labelled Illusion Dataset (LID) demonstrate that our framework significantly reduces hallucination rates while remaining practical for real-time deployment in resource-constrained environments. Code is available at: this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2611.15809
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RUMI: A Robotiq-Matched Universal Manipulation Interface for Robot Learning with Augmented Reality and Haptic Feedback
2026-11-11 18:17:42Kevin Avdiu, Seongjin Bien, Yannik Blei, Philine Meister, Johannes Hechtl, Philipp Schmitt, Kai Wurm, Georg von Wichert, Wolfram BurgardAbstract
Many manipulation tasks require carefully controlled contact forces for successful execution, yet large-scale, force-aware datasets remain scarce. Handheld interfaces can help by transmitting interaction forces directly through the operator's arm, but most existing designs are tailored to simple linear grippers and do not match the nonlinear mechanics of widely used robotic grippers. We present RUMI, a handheld universal manipulation interface matched to the Robotiq 2F-85 with exchangeable fingertip sensors that preserves the target gripper geometry while enabling natural input before contact and force measurement after contact. By integrating the tracking system into a virtual reality headset, our method provides real-time augmented reality feedback about the kinematic feasibility of motion on the target robot platform. We further introduce a haptic feedback mechanism that supports force-aware teleoperation for simulated and real robots. A user study shows fewer episodes containing joint-limit violations with live robot visualization and pose initialization (3/50) than without augmented reality (16/50). We additionally train imitation policies from demonstrations, collected using two different fingertip modalities enabled by the gripper-matched hardware, and assess the haptic interface's capability for collecting force-aware teleoperation datasets in simulation.
Abstract (translated)
URL
https://arxiv.org/abs/2611.15802
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SO-Arena: Benchmarking the Capability Spectrum and Reliability of System One Models
2026-11-11 17:53:45Lichen Li, Ziping Dong, Sicheng Feng, Qi Li, Xinchao WangAbstract
System One models promise fast, general-purpose decision-making without lengthy token generation. However, understanding their capability spectrum and decision reliability requires more than aggregate performance on current benchmarks. Existing evaluations often provide limited insight into the breadth of model capabilities and the reliability of their decisions. We introduce SO-Arena, a benchmark characterizing the capability spectrum and decision reliability of System One models across 11 domains and five capability families. For the capability spectrum, SO-Arena offers evidence-grounded evaluation through reliable sources and manual verification of reference labels against model-visible inputs; fine-grained capability assessment through domain-specific difficulty criteria; and realistic interactive evaluation, where models iteratively execute actions in sandboxes. Reasoning-intensive tasks further enable system one-two comparison. Beyond accuracy, SO-Arena assesses decision reliability through two complementary measures: decision stability under controlled option-set changes and missing-answer recognition when no valid option is available. Evaluations across 21 models reveal that the benefits of scaling and additional reasoning vary substantially across domains. More importantly, strong overall performance does not necessarily imply reliable decision-making: the best-performing System One model ranks only seventh in reliability. Together, these findings highlight the need to characterize capability spectrum and decision reliability as complementary dimensions, providing a systematic view of the strengths and limitations of System One models.
Abstract (translated)
URL
https://arxiv.org/abs/2611.15777
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Prior-as-Skill: Remember Outside and Read Inside with On-Policy Self-Distillation for Self-Evolving VLM Agents
2026-11-11 17:44:37Yan Lin, Jingyu Sun, Yifei Qian, Zhengtao Yao, Yuxia Wang, Daizong Liu, Chun Pong Lau, Zhongliang Guo, Stephen McGoughAbstract
Self-evolving visual agents keep what they learn as skills: key frames with step notes on a task's steps and their order. On-policy self-distillation (OPSD) is now a popular way to train such skills into a vision-language model that matches, on its own answers, a teacher that also reads the written order. At deployment, frames often go missing or show an unusual order, as when a person or a website reorders the steps. Existing internalization methods keep any order they store in the frame-reading weights: trained on full frames, the model guesses when they are missing, and trained with frames withheld, it stores the order, so memory can override what it sees. We propose PriorSplit, which fills in missing frames from an order kept outside the model, with no training beyond OPSD. It counts which of two neighboring steps comes first in training episodes, and the model checks if both asked steps are in view: if so, it answers from the frames, and otherwise the counts answer. Extensive experiments on synthetic skills with five models and on step-tagged order questions in new episodes of seen web, cooking and egocentric tasks demonstrate that, with frames missing, PriorSplit outperforms every compared method at OPSD's training cost. With Qwen3-VL-4B on synthetic skills, it also stays close to OPSD on unusual orders, where methods that gain most by storing the order fall behind.
Abstract (translated)
URL
https://arxiv.org/abs/2611.15766
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DISCERN: Preserve at Write, Resolve at Read for Persistent LLM-Agent Memory
2026-11-11 17:06:07Chirag Ahuja, Vladislav Tanov, Ramcharan Kakarla, Jonathan TawsAbstract
Enterprise agents accumulate memory across dozens of sessions in which facts contradict, get superseded, and blur as similarly named entities collide in embedding space. The field treats this as a retrieval problem; we show it is not. On ENT-310, a synthetic enterprise sales-agent memory benchmark of 310 queries (210 answerable, 100 unanswerable), a vanilla Retrieval-Augmented Generation (RAG) reader over a memory store already matches strong memory systems on the recall pillars. Retention and currency are near-saturated, roughly 0.93 to 0.98 for every competent system, so recall is essentially solved. What remains unsolved is resolution: which entity a query refers to, which time-state is current, and whether to commit an answer at all or decline when evidence is insufficient. We propose DISCERN (DISambiguation and Cross-session Episodic memoRy Navigation), an agent-memory framework built on a single rule: preserve at write, resolve at read. At write time DISCERN builds a strictly additive bi-temporal provenance graph that never merges confusable entities and never drops superseded values. At read time a zero-LLM tool surface drives an agentic loop that navigates this graph and commits exactly one of four gated terminal actions. A four-judge LLM-as-a-Judge council scores two axes: answer correctness on answerable questions, and the answer-versus-abstain decision as a classification problem. On ENT-310, DISCERN is the best-calibrated system on abstention (F1 0.924 versus at most 0.802 across eight baselines) and leads macro answer correctness under equal pillar weighting (0.861 versus 0.806 for the strongest baseline), a lead driven by entity resolution (0.800, where no baseline exceeds 0.40). Applied with zero tuning to the full LoCoMo corpus, the same pipeline again leads macro answer correctness (0.663 versus 0.638) and stays among the best-calibrated on false-premise abstention.
Abstract (translated)
URL
https://arxiv.org/abs/2611.15729
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Terms of Care: Designing for Participation in Data Governance by Disability Communities
2026-11-11 16:58:37Madiha Zahrah Choksi, Reeda Shimaz Huda, Rita Faia Marques, Anja Thieme, Cecily MorrisonAbstract
Improving disability representation in AI-generated media requires community-specific datasets, yet openly sharing such data can expose communities to misuse, decontextualization, and exploitation. We examine how disability communities can participate in dataset governance through context-sensitive licensing. Working with three disability advocacy organizations, we conducted semi-structured interviews, an educational webinar and modular license activity to promote community participation in data stewardship. Our findings show that governance in these high-context communities relies on organizational memory (especially situated harms); both formal authority and informal relational practices; communication; and resulting trust. The modular license activity made organizational data choices more accessible and values-led, allowing communities to adapt Creative Commons clauses to express permitted uses, sector-specific restrictions, safeguarding requirements, and commitments to respectful representation. Yet, concerns remain about monitoring and enforcing appropriate data use. We contribute (1) an empirical account of governance tensions, (2) a practical modular approach, and (3) design implications for AI data platforms that support relational, context-sensitive governance beyond standardized, one-size-fits-all frameworks.
Abstract (translated)
URL
https://arxiv.org/abs/2611.15720
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Contour-Assisted Evolutionary Computation: Estimating Peaks From the Contour Lines of Evaluated Solutions
2026-11-11 15:53:55Jun Zhang, Tong Qian, Hua Ding, Zhi-Hui Zhan, Wei-Jie Yu, Jian-Yu LiAbstract
Evolutionary algorithms (EAs) evaluate many candidate solutions but use them mainly to rank individuals. This paper treats the evaluated solutions as the spot heights of a topographic survey and draws the contour lines they imply. The contour map decomposes the evaluated region into peaks: in two dimensions a contour tree with persistence separates significant peaks from interpolation ripples, and in more dimensions hill climbing on the interpolated surface locates the peaks and ray-cast level sets describe them. A memory of the explored peaks directs the search to new ones. The data-supported contours of each peak are extrapolated to zero size by an area law, exact for quadratic peaks and second-order accurate for smooth ones, that estimates a peak's location, height and shape before it is sampled. A contour-assisted EA (CA-EA) inserts contour steps with local search and peak memory into a genetic algorithm (GA), differential evolution or particle swarm optimization. A contour-assisted surrogate-assisted EA (CA-SAEA), for expensive problems, spends each evaluation on surrogate-screened offspring or on sampling around a peak, entered at the maximum of the interpolated surface. On 2-D benchmarks, CA-EA reduces the evaluations a plain GA needs by factors of 12 to 112; controls attribute its gain over classical memetic search to restarting local search on distinct peaks. On the CEC 2015 expensive suite in 10 and 30 dimensions, CA-SAEA has the second-best average rank, behind DYCORS and ahead of lq-CMA-ES and the competition winner MVMO, although in 30 dimensions MVMO wins significantly on 9 functions and loses on 6; there the map beats sampling around the best point on 5 of 15 functions and loses on 3. Entering the peaks at their extrapolated locations brought no gain at these budgets, although, with enough samples, it improves the estimated heights on most two-dimensional functions.
Abstract (translated)
URL
https://arxiv.org/abs/2611.15641
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Marginal-Preserving Coupled Diffusion Composition for Long-Horizon Planning
2026-11-11 15:07:26Hengrui Zhang, Qiyu Wang, Yuhu Cheng, Brooks Paige, Xuesong WangAbstract
Compositional diffusion planners extend short-horizon trajectory models to long-horizon goal-conditioned planning by generating and stitching multiple local trajectory chunks. However, in multimodal environments, local plausibility is not sufficient for global consistency: independently initialized chunks may denoise toward incompatible overlap modes, producing plans that are individually feasible but incoherent after stitching. Existing remedies typically add search, guidance, pruning, or graph-based structure at inference time, increasing computational cost or relying on task-specific machinery. We instead investigate graph-structured dependence among chunk-wise initial diffusion noises as a lightweight coordination mechanism for compositional trajectory planning. We propose \textbf{M}arginal-Preserving \textbf{C}oupled \textbf{D}iffusion \textbf{C}omposition (MCDC), a training-free inference method for pretrained compositional diffusion planners. Instead of sampling chunk noises independently, MCDC constructs coupled Gaussian initializations whose per-chunk marginals remain exactly standard Gaussian, as required by the pretrained denoiser, while neighboring chunks receive positively correlated noise components. This preserves the standard-Gaussian initialization marginal of each chunk while biasing adjacent chunks toward compatible overlap-mode choices. We prove exact marginal preservation and show, under a simple multimodal overlap model, that positive coupling increases the probability of compatible mode selection. On OGBench long-horizon stitch tasks, MCDC consistently improves over independent compositional diffusion sampling while retaining nearly the same inference time, and achieves a favorable success--cost trade-off compared with search-based composition methods.
Abstract (translated)
URL
https://arxiv.org/abs/2611.15585
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TraceKV: Policy-Preserving Low-Rank KV Cache Compression for Reasoning Vision-Language Models
2026-11-11 14:54:19Ke Li, Zishan Shao, Yiran ChenAbstract
Reasoning vision-language models (VLMs) impose substantial KV cache demands from both large multimodal contexts and growing reasoning traces. Low-rank KV compression has emerged as a promising paradigm that stores low-dimensional latents of KV states and can be combined with quantization. However, existing low-rank methods often induce growing predictive policy drift from the dense model when compressing reasoning KV states, resulting in reasoning failures. We identify two key causes of this degradation: calibration-state mismatch between static contexts and on-policy reasoning states, and unequal error sensitivity across KV states. To address these challenges, we propose TraceKV, a training-free low-rank KV compression framework that unifies on-policy calibration, sensitivity-aware SVD, and global rank allocation under a single policy-preserving principle, substantially reducing policy-KL divergence during reasoning. Experiments across multiple VLMs and visual reasoning benchmarks demonstrate superior accuracy--memory trade-offs, achieving near-lossless reasoning accuracy with 5.64$\times$ KV cache compression when combined with quantization. Optimized low-rank attention kernels further deliver up to 1.83$\times$ end-to-end decoding speedup. Code will be available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2611.15569
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Faithful in Steps: Improving Generalization and Citation in RAG via Query Decomposition
2026-11-11 14:22:09Yue Liu, Zhongying Ru, Shimin Di, Jipeng Zhang, Ruiyuan Zhang, Xiaofang ZhouAbstract
Retrieval-augment generation is a prevalent strategy to mitigate hallucinations of LLMs. The attributable RAG (RAGQ) generates quotes for its answers. The quotes indicate which input contexts support the RAG to derive the answers, enhancing the answer's verifiability and trustworthiness. However, existing RAGQs exhibit significant degradation when dealing with questions that require multi-hop reasoning and multi-modal understanding, suffering from over-citation, implicit entity identification failure, and poor generalization. In this paper, we propose a novel RAGQ framework, namely QDRAG. QDRAG breaks down the input question into atomic subquestions to identify the implicit entities. Then, the reranker prunes context distractors to eliminate the downstream over-citation. To facilitate query decomposition, we propose two zero-shot approaches: QD-C and QD-R, which guide the QD MLLM to decompose the question based on context knowledge and retrieval rewards, respectively. One interesting finding is that finetuning on the QD task shows better generalizability compared to directly finetuning on the downstream RAGQ task. Experiments on four multi-modal QA benchmarks demonstrate QDRAG's efficacy in grounding answers and generating faithful citations. The framework significantly outperforms all the baselines on both in-domain and out-of-domain tests, even surpassing Gemini-Pro.
Abstract (translated)
URL
https://arxiv.org/abs/2611.15538
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