Artificial Intelligence · Cross-listed from cs.RO
Affordance-Conditioned Decision Making: Bridging the Semantic-Spatial Gap in Zero-Shot Cross-Floor Vision-and-Language Navigation
Xuekang Yang, Lu Chen, Shuang Luo et al. (+4 more)
Vision-and-
language navigation increasingly relies on general-purpose semantic planners, yet translating correct high-level intent into reliable physical execution remains difficult in spatially constrained transitions. Reaching a staircase, doorway, or narrow passage does not ensure traversal; the
agent must identify an executable affordance pose and recover from accumulated action errors. We propose PACE (Preference-refined Affordance-Conditioned Execution), a supervised local execution module that augments frozen zero-shot semantic planners for reliable cross-floor
navigation. PACE grounds transition-related semantics into a long-horizon,
agent-centric traversable affordance pose and conditions short-horizon action generation on this spatial target, thereby aligning semantic goals with physical execution. We further post-train PACE through failure-aware preference refinement using rollout-derived pairs that contrast normal or recovery behaviors with deviation-amplifying behaviors, thereby improving closed-loop correction. We integrate PACE into six open-source zero-shot VLN navigators and demonstrate consistent improvements on the cross-floor subsets of R2R-CE and RxR-CE, increasing the average success rate from 16.35% to 27.65% and from 4.76% to 12.06%, respectively. Real-world experiments further demonstrate PACE's applicability in unseen environments, highlighting the potential of traversable affordances to bridge semantic intent and reliable
embodied behavior.
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All authors Xuekang Yang, Lu Chen, Shuang Luo, Jialing Zhu, Qi Zhang, Yue Gao, Xiang Zhang
Artificial Intelligence · Cross-listed from cs.LG
A Solvable Theory of Pre-training Data Poisoning: Regime-Dependent Scaling Exponents
Indranil Halder, Rastri Dey and Cengiz Pehlevan
Pre-training data poisoning of large
language models is usually studied using targeted
backdoors and their survival through safety post-training, which leaves open a more basic question: how does a model's clean data performance degrade as the poison rate $\varepsilon$ grows? Motivated by our controlled pre-training runs of OLMo-style models, in which the relative clean data validation perplexity increase $\Delta$ between poisoned and clean models matched in architecture,
token budget, and optimization schedule is well fit by a power law $\Delta\approx C\varepsilon^{a}$ with a non-integer exponent, we ask what such a law requires theoretically. We first prove an analyticity barrier: whenever the contaminated objective depends analytically on $\varepsilon$ around a nondegenerate clean data optimum, $\Delta$ is generically quadratic in $\varepsilon$, so a generic non-integer exponent is a signature of genuinely singular structure. We then supply that structure in solvable truncated ridge regression with heavy-tailed
covariates, controlled by $q_\star$, and a label-shift poisoning. Our central result is that the excess risk scaling exponent depending on the order of limits: in the higher dimensional proportional regime it is $\epsilon^{q_\star/(q_\star+2)}$, whereas taking the ample-data limit first gives $\epsilon^{2-2/q_\star}$, and the limits do not commute. We confirm this prediction through several numerical simulations. Finally, we argue that finite training time plays the role of truncation on the
curvature spectrum in local LLM pre-training, deriving the observed scaling law under heavy tailed inverse
curvature spectrum as a modeling hypothesis.
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Artificial Intelligence · Cross-listed from cs.CL
Supporting and Performing Culture from the Inside
Lea Frermann and Steven Bird
Anthropology and related disciplines which study culture have often found it useful to consider their epistemological and methodological approaches in terms of emic versus etic. This is the distinction between the insider perspective, how the world is viewed by a member of the culture, versus the outsider perspective, the scientific cataloguing of cultural knowledge and practice. We adopt this framework to systematically analyse assumptions in recent research on culture in NLP along the full pipeline of task selection, data collection, system design and evaluation. We draw
attention to the `culture of NLP' which shapes the research focus and approaches of our field, and suggest pathways towards a more culturally attuned AI.
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Artificial Intelligence
When Does a Skill Add Value? Task-Conditional Gain Prediction for Selective Skill Use
Anjie Xu, Zhiyu Zhang, Ruiqing Ding et al. (+2 more)
Agent skills are expected to improve task performance. Yet we find that they often provide no benefit, and can even hurt performance while incurring additional
token costs. Can we predict whether a skill will help before the
agent acts? We introduce SkillDelta, a framework for predicting task-conditional skill gains from paired executions of the same
agent with and without the skill. A local predictor transfers these historical gains to new tasks without retraining the
agent. Under explicit transfer assumptions, our analysis links support coverage, representation mismatch, and execution noise to prediction error and decision regret. Across five
benchmarks and three target
agents, paired history improves observed-gain ranking over skill-assisted outcomes alone in 12 of 15 settings. At matched expected skill-use rates, SkillDelta improves success over random activation in all 15 settings, with an average absolute gain of 4.3%. Most of this advantage comes from allocation across task groups. Evidence for additional within-group selection value is strongest on ToolQA and weaker elsewhere. Code is available at https://github.com/TankTechnology/skilldelta.
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All authors Anjie Xu, Zhiyu Zhang, Ruiqing Ding, Fengli Xu, Leye Wang
Artificial Intelligence
Prefill-Free Cross-Family KV Cache Transfer for Heterogeneous Multi-Agent LLMs
Vincent-Daniel Yun, Woosang Lim, Haneul Yoo et al. (+3 more)
Recent multi-
agent LLM systems increasingly combine heterogeneous models for specialized
agent roles. However, text-based communication requires each receiver to prefill shared context already processed by the sender. Reusing the sender's key-value (KV) cache avoids this redundancy, but prefill-free transfer across model families must handle differences in tokenization, model depth, and KV representations. To address these issues, we propose HeteroFold, a prefill-free cross-family KV cache transfer method that keeps both the sender and receiver frozen. HeteroFold aligns model structures, maps the sender cache into the receiver space, and calibrates it to preserve receiver behavior. Across six transfer directions, HeteroFold achieves the best cache-transfer performance on all four long-context
benchmarks and most short-context settings. It also matches text-based communication on the multi-
agent benchmark. At 32K context length, Llama-3.1-8B$\rightarrow$Ministral-3-14B transfer is $10.7\times$ faster than Native Prefill and $1.18$–$1.47\times$ faster than the state-of-the-art prefill-free baselines, Dense Latent and KV Ridge. These results show that HeteroFold enables efficient cross-family KV reuse without receiver prefill.
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All authors Vincent-Daniel Yun, Woosang Lim, Haneul Yoo, Sungjoo Yoo, Murali Annavaram, Sai Praneeth Karimireddy
Artificial Intelligence
LAM: Efficient Lossy Agent Memory Framework With A Retrieval-Score Error Bound
Baixi Sun, Le Chen, Anjir Ahmed Chowdhury et al. (+10 more)
Agent memory grows as
agents read inputs, reason, and call tools. Longer histories increase inference cost and eventually exceed the context window. LLM-based summarization reduces this history but adds latency and provides no explicit bound on information loss. We propose LAM, a Lossy
Agent Memory system with three components: a deterministic deduplication rule with a substitution bound on
retrieval scores - a bound on score perturbation, not a certificate of unchanged ranking; a
memory manager that preserves the cached prefix and overlaps compaction with inference; and a performance model that estimates compaction costs before deployment. On 600
agent trajectories, LAM removes 22.47% of observation
tokens while retaining 99.984% of the measured gold-patch evidence. At a fixed deletion set, the performance model predicts a 71.4x-91.6x end-to-end speedup from removing records before prefill instead of deleting them from a prefilled context. That benefit comes from the schedule rather than the rule and applies to any prefix-preserving test.
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All authors Baixi Sun, Le Chen, Anjir Ahmed Chowdhury, Xiaolong Ma, Chih-Hsuan Yang, Mingze Xia, Syed Zawad, Sheng Di, Rajkumar Kettimuthu, Huihuo Zheng, Rajeev Thakur, Venkatram Vishwanath, Feng Yan
Artificial Intelligence
Clarify the User or Verify the World? Uncertainty Routing for Proactive Agents
Zhaofeng Li, Xuan Zhang, Xiaokui Xiao et al. (+1 more)
Tool-using LLM
agents must decide not only whether additional information is needed, but also which source can resolve the uncertainty. Existing proactive approaches often specialize in either user clarification or environment verification, without explicitly determining the appropriate information source for each decision. We formulate this problem as uncertainty routing among ACT, CLARIFY, and VERIFY, and propose PROUR, a proactive uncertainty routing framework. PROUR decomposes action uncertainty into disagreement across plausible user-goal interpretations, which signals user-side ambiguity, and the
entropy remaining within each interpretation, which signals missing world-side evidence. To acquire information from the routed source, a query generator is trained with a mode-conditioned information-gain reward, targeting user-goal identification under CLARIFY and next-action identification under VERIFY. On $\tau$-bench, PROUR achieves 28.17% average success rate across retail and airline, outperforming the strongest prior method by 4.57% while using 2.17 fewer interaction steps. The learned policy further generalizes to stronger task
agents and transactional domains of $\tau^3$-bench without retraining, demonstrating the benefit of source-aligned uncertainty resolution for proactive
agents.
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All authors Zhaofeng Li, Xuan Zhang, Xiaokui Xiao, Yang Deng
Artificial Intelligence
Why Directly Learning Periodic Trajectories Can Fail
Kaixin Zheng and Anita Layton
Operator learning of periodic solutions requires deciding how simulation data should be recorded and represented. A natural choice is to integrate long enough for transients to decay and record a window wide enough to contain at least one full period of all trajectories. We find that these conservative choices can make the resulting trajectories difficult to learn, even when the underlying periodic orbits vary regularly with system parameters. Unaligned trajectories generalize poorly even within the training distribution. Phase
alignment substantially improves in-distribution generalization, but models trained on a fixed physical-time window still have large errors on trajectories with periods outside the training range. We explain both failures through a common mechanism: frequency differences accumulate over time, so the target phase varies rapidly with the parameters. Predictors that cannot track this variation incur a population MSE floor in both settings; for fixed window prediction, we also derive a per-sample lower bound. We then study one of the simplest representations that escape these floors: learning an aligned, normalized waveform and its period separately. We establish regularity of the decoupled targets under ODE assumptions and show experimentally that this approach avoids both failures in ODE systems and a PDE case study.
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Artificial Intelligence · Cross-listed from cs.RO
DS-VLA: A Dendritic-inspired Vision-Language-Action Model for Robust Action Control
Yaxing Lyu, Jingyi Li, Mingkun Xu et al. (+1 more)
Vision-
language-action (VLA) models have achieved strong performance in
language-conditioned manipulation, yet success under nominal evaluation does not necessarily translate into robust closed-loop behavior when executed actions are transiently corrupted. We introduce DS-VLA, a dendritic-inspired action architecture that incorporates dendritic spiking dynamics into VLA control to address this limitation. Specifically, to enable modularized feature processing and temporal information integration, DS-VLA equips action
neurons with multiple sparsely connected dendritic branches, each featuring heterogeneous, learned decay factors. Furthermore, to suppress unreliable state updates while preserving task-relevant historical information, we introduce a
neuron-wise inhibitory gate that adaptively regulates the admission of new
multimodal evidence into dendritic states prior to somatic dynamics. We evaluate DS-VLA on all four LIBERO suites under both nominal rollouts and a unified closed-loop action-perturbation protocol. DS-VLA achieves a 91.6% average nominal success rate and an 87.35% average perturbed success rate, retaining 95.4% of its nominal performance. Under the same reported perturbation setting, OpenVLA-OFT, FAST, $\pi_0$, and GR00T achieve 39.45%, 23.90%, 28.55%, and 30.75%, respectively. A controlled ablation isolates the contribution of
neuron-wise shared inhibition, while analyses of neural dynamics and post-perturbation trajectories associate robust performance with selective evidence suppression and effective behavioral recovery. Together, these results demonstrate that integrating
brain-inspired computational mechanisms offers a promising architectural prior for robust
embodied intelligence beyond merely scaling
vision-
language backbones or generative action decoders.
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All authors Yaxing Lyu, Jingyi Li, Mingkun Xu, Yujie Wu
Artificial Intelligence
Certifying Interventional Agreement Among Observationally Equivalent Causal Models
Sourena Khanzadeh, Daniel Platnick, Marjan Alirezaie et al. (+1 more)
Observationally equivalent causal models can still disagree about what happens under intervention, because interventions create inputs that never occur in observational data. We introduce Interventional Separation Selection (ISS), which repeatedly queries the true system with an admissible intervention on which the surviving candidate models disagree, discards the candidates the outcome contradicts, and stops once no intervention within a cost bound separates the survivors. If the true system is among the candidates, this stopping condition certifies that every survivor agrees with it on every admissible intervention within the bound, a guarantee that no observational learner can give, however much data it sees. The stopping condition depends only on the survivors, so it can be checked without knowing the truth. For continuous variables the candidates form an infinite version space, and mixed-integer linear programs decide the stopping condition exactly over all of it, with agreement holding up to a tolerance. On a three-digit colored MNIST causal abstraction task in which ink hue tracks digit size, plain convolutional networks trained on examples reach zero held-out error, yet disagree with shape-based labels on 26% of single-digit edits, as often as hue-based labels do.
Auditing the causal abstractions of networks observed only on such images, ISS certifies what each network perceives with 13.6 interventions per image on average, and each certificate, checked against every admissible intervention, holds whenever the network's true abstraction is among the candidates. When a network bypasses a unit that every candidate abstraction relies on, certificates covering interventions on that unit can be silently void, and twenty random validation interventions refute 69% of them.
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All authors Sourena Khanzadeh, Daniel Platnick, Marjan Alirezaie, Hossein Rahnama
Artificial Intelligence · Cross-listed from cs.CE
AutoPDEBench: Benchmarking LLM Auto-Research for Neural PDE Solver Design
Ruoyan Li, Wei Wang and Yizhou Sun
Partial differential equations (PDEs) are essential for modeling complex physical systems, and neural solvers have recently emerged as powerful data-driven tools for numerically solving them. However, existing neural solvers struggle with domain-specific challenges, such as varying parameters and high-speed flows, necessitating specialized architectures. Manually designing these specialized solver architectures is a highly iterative, time-consuming process requiring deep expertise, creating a significant bottleneck in scientific discovery. We propose leveraging autonomous AI research
agents to automate the synthesis of specialized solvers. To support this, we introduce AutoPDEBench, a
benchmark dedicated to LLM-driven automated research for PDE solver design. The
benchmark includes 25 challenging
datasets featuring both novel and actively studied physical scenarios. We evaluate a suite of general-purpose models (
transformer, ROM, and graph-based) alongside a multi-
agent instantiation of the iterative automated research pipeline, which serves as an agentic baseline. Empirical results show that the iterative automated research system significantly outperforms the general-purpose neural solver baselines. Our findings demonstrate the viability of using AI
agents to automatically design neural solvers for complex physical systems. AutoPDEBench provides a foundational testbed to accelerate
agent-driven scientific discovery in physics and engineering.
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Artificial Intelligence · Cross-listed from cs.NI
Toward Agentic Optical Networks: A Vision of LLM Agent-Driven Autonomous Lifecycle Management
Yao Zhang, Shengnan Li, Yuchen Song et al. (+11 more)
As optical networks continue to expand in scale, complexity, and service diversity, the implementation of automation has become essential for ensuring agility, efficiency, and reliability in lifecycle management (LCM) of optical networks. Large
language model (LLM)
Agent, distinguished by its progressively sophisticated capabilities in logical
reasoning, adaptive decision-making, complex problem solving, and multi-task orchestration, presents great opportunities to advance network automation beyond traditional AI techniques. Nevertheless, the application of LLM
Agent in optical networks remains in its early exploratory stage, challenged by the lack of multi-task coordination, high computational demands, data dependence, and reliability concerns. In this paper, we envision a conceptual roadmap toward Agentic Optical Networks (AONs) by integrating LLM
Agents throughout the LCM with high-level autonomy. First, we trace the
evolution from manual operations to AI-empowered frameworks and distill key technologies in
Agent, providing actionable insights into leveraging its strengths for addressing practical network automation challenges. A core contribution of this paper is the proposal of a hierarchical multi-
Agent framework, which is specifically developed to manage every phase in LCM of AONs, including planning, deployment, operation, maintenance, upgrade, and decommission, thereby enabling more cohesive and comprehensive automation throughout the entire lifecycle. In addition, future directions and underlying challenges are also discussed at the intersection of LLM and optical networks. By aligning the LLM
Agent with the specialized requirements of AONs, this work aims to explore the potential for the
evolution of optical networks moving from task-level semi-automatic execution toward lifecycle-level full autonomy.
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All authors Yao Zhang, Shengnan Li, Yuchen Song, Yidi Wang, Yue Pang, Wenbin Chen, Xiaotian Jiang, Xiao Luo, Meixia Fu, Min Zhang, Yongli Zhao, Shanguo Huang, Alan Pak Tao Lau, Danshi Wang
Artificial Intelligence · Cross-listed from hep-lat
LaMET-Agent: An Agent Framework for Large-Momentum Effective Theory Analysis
Jinchen He, Xiangyu Jiang, Fei Yao et al. (+1 more)
Large-momentum effective theory (LaMET) provides a first-principles framework for computing the $x$ dependence of light-cone parton distributions from
lattice QCD. Over the past decade, theoretical and numerical advances have established a mature multi-stage workflow for systematic calculation of parton physics, although its implementation still requires expert judgment and substantial repeated effort. We present lamet-
agent, an open-source large
language model (LLM)
agent framework that organizes this workflow into an executable, reproducible, and inspectable analysis pipeline. The present release supports collinear
quark distributions and implements correlator analysis, renormalization,
Fourier transformation, perturbative matching, continuum, physical pion mass and infinite-momentum extrapolations, and automated result review. We validate it on four end-to-end analyses: pion parton distribution functions in the gauge-invariant and Coulomb-gauge formulations, and pion and kaon distribution amplitudes, obtaining results consistent with the published calculations. Extensions to transverse-momentum-dependent distributions, generalized transverse-momentum-dependent distributions, and gluonic distribution functions are planned for subsequent releases.
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All authors Jinchen He, Xiangyu Jiang, Fei Yao, Dian-Jun Zhao
Artificial Intelligence
RAO-Nav: Probing Omni-Language Models for Zero-shot Semantic Audio-Visual Navigation
Qilang Ye, Meng Liu and Yu Zhou
We explore whether Omni-
Language Models (OLMs) can be directly applied to zero-shot Semantic Audio-Visual
Navigation (SAVN). Recent work demonstrates that even state-of-the-art specialized models still struggle to achieve generalist
multimodal navigation, despite extensive task-specific training. In this paper, we introduce RAO-Nav, short for
Reasoning All-in-One OLM, a deployment pipeline for zero-shot SAVN. By leveraging the rich implicit audio-visual knowledge encoded in OLMs, the
embodied agent is enabled to “hear”, “see”, “reason”, and “act” in the environment. To further elicit the built-in thinking ability of OLMs, we propose a test-time Latent
Navigation Reasoning (LNR) module that can be seamlessly integrated into the decoding space. LNR encourages the model to retrieve more target-relevant observations and make effective
navigation decisions. Through comprehensive experiments, we show that our framework surpasses existing state-of-the-art baselines on public SAVN
benchmarks without using any training data. Moreover, we introduce a new Global
Navigation Instruction setting to further evaluate the ability of OLMs to serve as
embodied navigation agents. Code: https://github.com/rikeilong/OmniAV\_Nav.
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Artificial Intelligence
A bilingual AI audiologist built through rubric-guided closed-loop engineering outperforms human audiologists in a blinded evaluation of simulated cases
Linkai Li, Changgeng Mo, Hanlin Yu et al. (+4 more)
Audiology consultation requires structured history-taking, audiometric interpretation and patient-centred communication, yet real-world case material is scarce. We present a bilingual AI audiologist pairing a general-purpose large
language model with rubric-guided closed-loop engineering,
multimodal audiogram interpretation and
retrieval-augmented grounding, without fine-tuning the
language-model backbone. Using a 21-item rubric and an AI patient simulator, we distilled experience-derived guidance into a 19-rule playbook from 73 training cases (43 English, 30 Chinese) and evaluated the system on 58 independent simulated cases (30 Chinese, 28 English) in a pre-specified, source-blinded comparison with 17 practising audiologists. The AI audiologist outperformed human audiologists on every case (58/58; mean paired $\Delta$ = +1.35 on a 5-point composite, Cohen's d = 1.84, $P = 4.5 \times 10^{-20}$), on 20 of 21 rubric items and in both
languages. Component ablation identified the playbook as the largest contributor, offering a practical route to specialist consultation
agents in low-data medical domains.
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All authors Linkai Li, Changgeng Mo, Hanlin Yu, Congxi Lu, Shangqiguo Wang, Matthew B Fitzgerald, Shan X Wang
Artificial Intelligence · Cross-listed from cs.LG
DP-Rec: Towards Dynamic Patching for Efficient Long-Sequence Recommendation
Dwipam Katariya, Thomas Caputo, Akshat Shreemali et al. (+6 more)
Transformers have redefined sequential recommendation by effectively modeling dynamic user behaviors and long-range dependencies. However, they remain inherently inefficient: standard architectures operate at a fixed rate, allocating comparable computation to every item in a user's history regardless of its information content. This leads to prohibitive computational overhead on long sequences and increased sensitivity to behavioral noise. To address this, practitioners often resort to lossy sequence compression, staged modeling, or truncation. This limits the model's ability to leverage the full context of long histories during inference. Inspired by the recent success of Byte Latent
Transformer, we propose DP-Rec, a dynamic latent patching architecture for recommendation. DP-Rec shifts from item-level modeling to patch-level modeling by segmenting interaction sequences using contrastive
entropy surprise to identify informative behavioral boundaries. A lightweight patch encoder compresses these temporally contextualized segments into a reduced set of dynamic latent behavior vectors, which are then processed by a larger latent
transformer and decoded for next-item prediction. Extensive experiments show that, under constrained computational budgets, DP-Rec scales effectively to long sequences and achieves a superior efficiency-accuracy trade-off over both non-compressed and fixed-size compression baselines.
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All authors Dwipam Katariya, Thomas Caputo, Akshat Shreemali, Juan Manuel Origgi, Nikita Seleznev, Pranab Mohanty, Kalanand Mishra, Nam Nguyen, James Montgomery
Artificial Intelligence · Cross-listed from stat.ME
Rank Confidence Sequences:Anytime-valid Leaderboards
Hamed Khosravi and Xiaoming Huo
Leaderboards rank models by their average scores on
benchmark items, and they are consulted repeatedly while the evaluation is still running. Existing confidence intervals for a model's rank control their error rate only if they are computed once, after a number of items chosen in advance. If they are recomputed as results arrive, and the evaluation stops once they look decisive, their error rate exceeds its nominal level. Anytime-valid methods keep their guarantees at all sample sizes simultaneously and hence under any stopping rule. They exist for the accuracy of one model, for one pair of models and for the set of models that may be best. For pairwise battles they also give ranks. None gives ranks when all models are scored on the same items, which makes their scores dependent. We construct rank confidence sequences: for every model, a set of ranks that contains its true rank, simultaneously for all models and at all times, at a chosen error level $\alpha$, in finite samples. The construction combines betting e-processes, one for each ordered pair of models, with closed testing over the possible orderings of the models. It allows any dependence between the models' scores on an item. The method has two advantages. A leaderboard can be inspected after every item without inflating its error rate. The evaluation of each model can stop as soon as the question asked about it is answered, which saves compute. When results are examined only once, halfway through or later, little power is lost relative to fixed-sample methods. The paper quantifies these advantages in simulations and on public leaderboard data.
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Artificial Intelligence
Fracast-0: Fractal Weight Sharing for a Time Series Foundation Model with Only 85K Parameters
Tianxiang Zhan, Huanyao Zhang and Yuanpeng He
Time series foundation models must preserve multi-domain breadth, probabilistic output, and multiple temporal scales, but parameter count grows when each scale receives a separate representation. We introduce Fracast-0, a probabilistic
forecasting foundation model that exploits temporal self-similarity to reuse one operator across scales. A parameter-free detector extracts significant seasonal structure. The encoder applies a shared local block along a
geometric dilation ladder with scale conditioning, while the decoder combines context-gathered states with an explicit seasonal future state and reuses a second block along another ladder before emitting nine
quantiles. Pretraining across six corpora preserves multi-domain breadth within 85,001 parameters. On 97 GIFT-Eval configurations without per-
dataset fine-tuning, Fracast-0 is the smallest of 28 evaluated checkpoints and remains non-dominated in the aggregate parameter-accuracy plane with MASE 0.808 and WQL 0.564. It uses 42.0% fewer parameters than TinyCast, whose MASE and WQL are 4.2% and 3.3% lower. These results support cross-scale weight reuse as a practical route to further time series foundation model compression.
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Artificial Intelligence
Witness: Discovery, Deciphering, and Epiphany in Interactive Puzzle Environments
Guanghan Ning, Ping Liu, Linyi Li et al. (+6 more)
Automated science needs
agents that can work out the rules of an unfamiliar environment by interacting with it. Interactive rule-discovery puzzles offer a controlled setting for studying this ability: an
agent infers hidden rules through experimentation and uses what it has inferred to reach a stated goal. We ask what limits current
language models on these puzzles and whether reinforcement learning (RL) improves performance on rules held out from training. To study both, we introduce WITNESS, a 2D grid-based puzzle environment with ground-truth ASCII observations and controlled access to rules. An agentic pipeline generates games for WitnessGym, the RL training suite, and WitnessBench, comprising public validation and private test games. The validation set separately tests new compositions of trained rule primitives and primitives absent from training. Under a shared harness, the best of 18 frontier proprietary and open-weight models solves only 24% of private test level slots, with scores sensitive to the observation interface and
agent configuration. Providing ground-truth rules raises Opus-5's validation RHAE-L5 (relative human action efficiency over the first five levels) from 59.9 to 97.8, whereas a 27B open-weight model gains only 2.1 points and remains limited even with the rules provided. RL on WitnessGym raises the 27B model's private test RHAE-L5 from 2.1 to 5.4 and yields a mean gain of 4.1 points on four external discovery
benchmarks. Together, these results point to rule acquisition as a major difficulty for frontier models like Opus-5 while smaller models further struggle on rule-based execution, and indicate that RL on hidden-rule puzzles transfers to broader rules and real-world tasks beyond training.
Benchmark is available at: https://witnessbench.ai
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All authors Guanghan Ning, Ping Liu, Linyi Li, Huangjie Zheng, Arjun Neervannan, Huu Nguyen, Michael Sklar, Deniz Zorlu, Nicolai Ouporov
Artificial Intelligence
Instruct, Not Answer: Using Instruction Privileges in On-Policy Context Distillation
Hantao Yu, Xiaoxue Han, Udaya Ghai et al. (+4 more)
On-Policy Context
Distillation (OPCD) has recently emerged as a powerful technique for transferring context to student models and for self-improvement. In OPCD, the teacher is conditioned on privileged information, and the goal is to minimize the Kullback-Leibler (KL) divergence between the privileged teacher and the student, evaluated on student-generated
tokens. Many existing studies show that using instance-specific gold answers or gold demonstrations as the default privilege can hurt training performance, especially out-of-distribution (OOD). In this work, we instead design general instructions that target common student mistakes observed on the training samples, and show that such simple instructions can outperform gold as the OPCD privilege. In autoformalization tasks, using a matched formatting instruction as the privilege could outperform gold in OOD accuracy by a large margin. In 7 out of 8 experiments using ProverQA, ProofWriter, and ProntoQA as
datasets, and Qwen3-Thinking and Olmo3-Thinking families as models, matched instruction privileges outperform gold in OOD by 4 to 17 points, while remaining on par with gold in-domain. Each instruction is only a few sentences (and thus contains much less information compared to all instance-specific gold) and is applied uniformly to every training sample. These results indicate that a general instruction, which applies equally to source and target domain examples, can be substantially more transferable than instance-specific gold in OPCD while maintaining in-domain performance.
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All authors Hantao Yu, Xiaoxue Han, Udaya Ghai, Ferhat Erata, Joseph Lilien, Aman Goel, Ali Torkamani
Artificial Intelligence · Cross-listed from cs.PL
Agents as Software: A Programming Languages Agenda for Agent Reliability
Shraddha Barke and Adithya Murali
AI
agents increasingly resemble software systems: they call tools, remember facts, follow policies, delegate work, and take actions with real consequences. % Yet the “program” of an
agent is scattered across
prompts, tools,
memories, workflows, and execution traces, making its behavior difficult to inspect through ordinary testing and debugging alone. % This essay argues that a programming-systems perspective offers a natural lens for making
agents reliable. % We recast
agents as programmable artifacts whose behavior can be specified over traces and state, checked before deployment, monitored during execution, and improved from observed failures. % The goal is not to make probabilistic
agents behave like deterministic programs, but to give them enough structure that their behavior can be reasoned about, controlled, and repaired.
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Artificial Intelligence
CoMemBench: Benchmarking Collaborative Memory Boundaries across Multi-Agent Workflow Topologies
Sen Zhao, Ruiqi Kong, Zuyu Zhang et al. (+5 more)
Multi-
agent workflows require task-relevant information to be shared across
agents, while irrelevant, stale, unverified, or incompatible information must remain isolated. We call this task-conditioned scope of information a collaborative
memory boundary. Workflow
topology determines which intermediate artifacts are applicable to which downstream workers and when they cease to be valid, thereby providing a structural stress dimension for sharing and isolation. Existing
memory benchmarks primarily evaluate retention and
retrieval, whereas multi-
agent benchmarks emphasize coordination and end-to-end completion, leaving
topology-conditioned
memory boundaries largely unmeasured. We introduce CoMemBench, an execution-grounded
benchmark for collaborative
memory sharing and isolation across multi-
agent workflow
topologies. It constructs 800 composite workflows across four domains from source-grounded dependency graphs, with node-local specifications, verifiable artifact handoffs, native evaluators, and matched isolation challenges. CoMemBench measures workflow completion, verified node progress, required-handoff reliability, isolation robustness, and
token cost. Experiments reveal a sharing-isolation trade-off: broader context improves information availability but can weaken isolation, while system rankings shift across
topologies and artifact violations.
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All authors Sen Zhao, Ruiqi Kong, Zuyu Zhang, Lifeng Shen, Xinyu He, Ding Zou, Xu Zhang, Qinghua Zhang
Artificial Intelligence · Cross-listed from cs.CV
Evaluating Single and Multi-Omics Based Explainable Artificial Intelligence (MOXAI) for Molecular Subclass Classification of Adult-Type Diffuse Gliomas
Md Zahangir Alom, Quynh T. Tran, Breuer Alexandar et al. (+1 more)
DNA methylation (DNAM) profiling has emerged as a powerful diagnostic tool for classifying
brain and solid tumors. However, existing computational models typically analyze methylation and copy number variation (CNV) data separately, failing to capture the complementary information their integration could provide. Moreover, current classification models lack mechanisms for within-class risk assessment analogous to traditional tumor grading, and no established explainability method can attribute classification decisions to specific genomic loci. In this paper, we present MOXAI (Multi-Omics Based Explainable AI), a deep learning framework that integrates DNA methylation and copy number data from methylation arrays to classify molecular subtypes of adult-type diffuse gliomas, alongside single-modality variants for comparison. Using a cohort from The Cancer Genome Atlas (TCGA), we trained ResNet50, DINOv2, and Graph
Attention Network (GAT) models on methylation data alone, copy number data alone, and combined
multimodal data. We further developed explainable AI (XAI) methods based on class activation maps (CAMs) and gradient-weighted CAM (Grad-CAM) to identify the specific CpG sites, genes, and chromosomal regions most relevant to each classification decision. The
multimodal model achieved up to 92.98% cross-validation accuracy, outperforming models trained on CNV data alone. DINOv2 showed the strongest generalization, reaching 94.25% accuracy (confidence >0.9) on independent validation sets. XAI results aligned with established molecular features of adult-type diffuse glioma subtypes, confirming the biological interpretability of the framework.
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All authors Md Zahangir Alom, Quynh T. Tran, Breuer Alexandar, Brent A. Orr
Artificial Intelligence · Cross-listed from cs.CV
Presence Is Not Faithfulness: Figurative Vehicle Intrusion in Text-to-Image Generation
Xiaoyu Ma, Chen Yang and Hao Chen
Text-to-image (TTI) models increasingly generate high-quality images from natural-
language prompts, yet figurative
language exposes a failure: a vehicle that should guide the depiction of a tenor may instead be rendered as a visible object. We call this failure Figurative Vehicle Intrusion: the intruding content is textually licensed, but it is assigned the wrong visual role, showing that visual presence is not always faithfulness and that presence-oriented evaluation can miss such errors. To study it systematically, we introduce Vehicle Intrusion and Semantic Tenor Assessment (VISTA), a
multilingual benchmark of figurative
prompts organized by Figurative Form and Mapping Mechanism. We further propose V-Score, a diagnostic question-answering metric that evaluates role-aware figurative faithfulness in generated images. Evaluations on recent high-performing TTI models show that vehicle intrusion persists across
languages and figurative categories. As a lightweight mitigation, we introduce VISTA-Guard, which partially reduces vehicle intrusion and suggests a practical path toward more figuratively faithful TTI generation. All resources will be released publicly.
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Artificial Intelligence
AI Harness: Certification under Proposal-Conditioned Information for Foundation-Model Agents
Hailin Zhong and Shengxin Zhu
Foundation-model
agents are often modeled as policies over an observed state. In deployed systems, however, a runtime may intervene only after the model has emitted a semantic proposal, making the proposal both an action candidate and a decision-time observation generated by a history-conditioned process. We show that collapsing this structure into a state-only proposal envelope can preserve proposal coverage while destroying certifiability. In a finite robust interface, the viability kernel of the collapsed model is contained in the physical projection of the history-augmented kernel, and the collapse is lossless exactly when every proposal-conditioned collapsed fiber retains a common robust-safe intervention. This gap can be maximal even with constant-size proposal and history alphabets. The same common-action condition yields a dual result: observing the current proposal can restore robust feasibility when it separates latent modes requiring incompatible interventions. We extend these one-step results over time using exact finite beliefs and standard safety and reachability fixed points, separating indefinite operational viability from finite worst-case verified progress. Controlled model-in-the-loop tests reproduce the predicted obstructions when telemetry or effect verification is removed or intervention authority is restricted. Thus, our contribution is not a new fixed-point calculus, but a characterization of when proposal–history correlation at the model–tool boundary is necessary for certification.
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Artificial Intelligence · Cross-listed from cs.CV
KeyRec: Bounded Visual Memory for Streaming and Long-Video Understanding
Zihan Chen, Xuejian Rong, Xiaojuan Wang et al. (+4 more)
Vision-
language models are increasingly used to understand long
videos and continuous streams. However, dense visual
tokens accumulate with
video duration, making long-context inference prohibitively expensive. Existing training-free visual-
token selection methods reduce this cost by retaining informative
tokens, but may lose coherent event evidence and fail to distinguish detailed recent observations from long-range history. We propose KeyRec, a training-free framework for constructing bounded visual
memory. During query-agnostic writing, KeyRec preserves fine-grained recent observations in a visual cache and organizes historical evidence into a structured event bank. Candidate events are proposed according to their novelty relative to previously stored events and maintained through an online add–merge–evict update. When a question arrives, a text-only router adaptively allocates a fixed readout budget between recent and event
memory, without reprocessing historical frames. KeyRec operates on model-facing visual
embeddings and supports both modular encoder–projector VLMs and the encoder- and projector-free NEO-ov architecture. Across four streaming and long-
video benchmarks and three VLM backbones, KeyRec achieves the best compressed performance in 13 of 15 settings using only 10% of the dense decoder-facing visual-
token budget. It outperforms the strongest compressed baseline by 2.21–18.37 points on real-time questions, achieves the best compressed result in five of six long-
video settings, and performs best in every NEO-ov 2B setting.
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All authors Zihan Chen, Xuejian Rong, Xiaojuan Wang, Boqing Gong, Adi Zicher, Yael Pritch, Nikhil Karnad
Artificial Intelligence · Cross-listed from cs.CV
OneFixer: High-Quality and Consistent One-Step Autoregressive 3DGS Refinement for Driving Scenes
Boseong Jeon, Junhyeop Lee, Juhan Cha et al. (+1 more)
Autoregressive
video diffusion is a promising render-time fixer for 3D Gaussian Splatting (3DGS) in autonomous-driving simulation, but deployment demands high visual quality and temporal consistency at low latency. This is especially hard for one-step causal generation, where each imperfect prediction immediately becomes context for subsequent frames. Existing approaches stabilize rollouts through staged training with multiple modules and rollout-aware regularization, yet one-step quality still falls short of what deployment requires. We introduce OneFixer, a one-step autoregressive
video-
diffusion fixer trained in a single task-specific adaptation stage. Our key idea is a deployment-matched shared rollout: the model's own one-step predictions serve as the causal context for flow matching, exposing training to deployment-time errors, while the same rollout receives direct pixel-space perceptual supervision to preserve fine detail. Because the predictions optimized for current-frame quality are exactly those reused as future context, fidelity and autoregressive robustness are learned jointly, without bidirectional-to-causal conversion or teacher-student
distillation. OneFixer further exploits cues that driving simulation readily provides, lane geometry and dynamic-
agent states, to improve
geometric fidelity. On Waymo and proprietary driving scenes with 900-frame rollouts, OneFixer achieves the lowest FVD, LPIPS, and DISTS among all baselines at one step, with temporal consistency matching or exceeding multi-stage DMD pipelines. Under identical backbone and conditioning, it matches a multi-stage DMD-with-Self-Forcing pipeline in under half the GPU-hours and keeps improving beyond its plateau. In closed-loop simulation with a driving policy, OneFixer reduces the collision rate by a third relative to raw 3DGS rendering. Project page: https://onefixer-web.vercel.app/
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All authors Boseong Jeon, Junhyeop Lee, Juhan Cha, Hayoung Kim
Artificial Intelligence
Noisy Test-Time Reinforcement Learning for Code LLMs
Xikai Yang, Hieu Trung Nguyen, Dunyuan Xu et al. (+4 more)
Large
language models (LLMs) have demonstrated remarkable performance across various code-related tasks. However, unlike carefully curated
datasets that are typically high-quality and error-free, real-world user instructions are often vague and error-prone, posing significant challenges to the robustness of code LLMs. Furthermore, robustness-oriented fine-tuning relies on paired clean-noisy samples, which are costly to curate and require sophisticated noisy simulation techniques. To address these challenges, we propose the Noisy Test-time Reinforcement Learning framework (NTRL-Code), which enables robust self-
evolution of code LLMs using only unlabeled noisy data during the testing stage. Specifically, NTRL-Code uses conservative self-denoising to obtain a cleaner semantic anchor for target estimation, and employs an abstract-syntax-tree (AST)-based structural aggregation mechanism to estimate a proxy target from multiple candidate programs. The policy is then optimized on the original noisy
prompts with a hybrid reward that combines format validity, code similarity, and anti-repetition signals. Extensive experiments on three
benchmarks, each incorporating character-level, word-level, and paragraph-level perturbations, demonstrate that NTRL-Code yields robust and consistent improvements, stabilizing the predictions of various base models. Our code is available at https://github.com/Xikai97/NTRL-Code.
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All authors Xikai Yang, Hieu Trung Nguyen, Dunyuan Xu, Yuzhi Zhao, Jinpeng Li, Wenao Ma, Pheng-Ann Heng
Artificial Intelligence · Cross-listed from cs.LG
Uncertainty-Aware Selection of Online Algorithms with Simulator Ensembles
Yongyi Guo, Zifan Xu, Ziping Xu et al. (+1 more)
The performance of online reinforcement learning depends critically on design choices, especially those that affect exploration. These choices are often selected by fitting a simulator to offline data, evaluating candidate
algorithms in that simulator, and deploying the best-performing one. The simplest Plug-In selection rule simply selects the best performing
algorithm on the fitted simulator, making evaluations unreliable when the offline data used to fit the simulator are limited. We investigate Uncertainty-Aware selection, which forms an ensemble of simulators—for example, obtained by bootstrap resampling—and selects the online
algorithm with the best average performance across the ensemble. While ensemble-based approaches have been used to mitigate distribution shift and facilitate sim-to-real transfer, we formally show that this approach can mitigate the effects of limited data when fitting the simulator and theoretically has significant regret gains compared to Plug-In selection in multi-armed bandits. We also empirically investigate the Uncertainty-Aware selection approach in deep RL experiments on robotic control tasks that involve selecting reward-shaping hyperparameters, and show that it leads to more reliable selection and improved online performance.
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All authors Yongyi Guo, Zifan Xu, Ziping Xu, Kelly W. Zhang
Artificial Intelligence
PastForward: Faster On-Device GUI Agents via Computational Experience Reuse
Taehwan Park, Changmin Lee, Hayeon Lee et al. (+1 more)
Running GUI
agents on edge devices can keep sensitive screens and interaction histories local, but the computational cost of inference at every action step makes deployment challenging. Existing GUI
agent systems either perform full
vision-
language model (VLM) inference at each action step or reuse coarse-grained knowledge matched to prior tasks. However, dynamic mobile environments and user tasks make it difficult to fully utilize prior task executions without additional fine-tuning or task-specific offline exploration. To address this challenge, we present PastForward, a system that accelerates GUI
agents through validated, fine-grained reuse of computational experience accumulated during ordinary task execution. During decoding, PastForward retrieves prior output sequences as device-adaptive multi-
token proposals and verifies them in a single VLM forward pass. Across action steps, it uses prior GUI transitions to begin next-step inference while the device executes the current action, retains the early computation only when the predicted screen matches the observed screen, and carries reusable KV states forward. We evaluate PastForward on AndroidWorld workloads derived from real mobile usage patterns using multiple VLM backbones across server and edge platforms. On device, PastForward achieves action-step latency speedups of 1.63-2.36$\times$ while maintaining task success rates.
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All authors Taehwan Park, Changmin Lee, Hayeon Lee, Taesik Gong