Artificial Intelligence · Cross-listed from cs.CL
QuanLing: Cross-Branch Validation of Language Distance Quantification on Western Romance
Yiping Bai
Quantifying
language distance among closely related
languages remains a core challenge in quantitative linguistics. Our previous work [1] introduced QuanLing (Quantitative Linguistics via Pretrained
Language Models), a quantitative framework combining
language distance metrics (sentence
embedding distance, tokenization fragmentation rate) with
language property analysis (MLM prediction probability), validated on North Germanic (Danish, Norwegian Bokmål, Swedish). This paper extends QuanLing to Western Romance–French, Portuguese, Spanish, Italian–testing cross-branch applicability with the same metric family and aggregation protocol as our North Germanic study, adapted for four
languages (English anchor, quadruplet construction). Using 150 four-
language parallel sentences, we compute LaBSE sentence
embedding distances, tokenization fragmentation rates from four monolingual BERT tokenizers, and mBERT masked
language model mutual intelligibility. Results show that Portuguese–Spanish are closest (LaBSE distance 0.0229), French–Italian most distant (0.0338); LaBSE and mBERT rankings agree on 4 of 6 pairs, confirming cross-model robustness. Western Romance shows a wider absolute distance span than North Germanic (0.011 vs. 0.008) but comparable relative ratios (1.48 vs. 1.67), consistent with longer divergence time. French exhibits notably higher MLM predictability (36.12% top-1 accuracy vs. 29.28% for Italian), reflecting its orthography–phonology decoupling. This cross-branch validation provides further evidence for QuanLing's generalizability beyond a single
language branch.
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Artificial Intelligence
AegisFlow: A Multi-Agent Agentic AI Framework for Autonomous Remediation and Self-Healing in Fragile Data Ecosystems
Muhammad Bilal Awan, Zubair Hussain and Abdul Shahid
Traditional data pipelines are notoriously brittle, often failing due to upstream schema drift, API contract changes, or website DOM modifications. Present observability tools only raise alerts but for human engineers, resulting in a high Mean Time to Repair (MTTR) and operational fatigue. In this paper we propose AegisFlow (Agentic Engine for Intelligent Self-healing and Graph-driven Operations for Workload remediation), a novel agentic framework that closes the loop between detection and resolution. AegisFlow uses a Watchdog
agent to collect runtime telemetry and has a Repair
agent to automatically create, test and deploy code patches based on Large
Language Models (LLMs). The framework presents the non-intrusive execution model called Parallel Shadow Patching, a non-intrusive execution model based on the Monitor, Analyze, Plan, Execute, Knowledge (MAPE-K) loop to generate and verify patches in digital twin environments. Through experimental testing, we have evaluated AegisFlow across five common failure scenarios, and see 98.1 percent improvement in MTTR (from an average of 170 minutes per patch to 3.2 minutes) and a patch success rate of 92 percent . In particular, the system is successful in dealing with changes in the JSON schema (96 percent ) and punctuation drift (98 percent ), and is least successful in Shadow DOM cases (85 percent ). AegisFlow frees up about 98 percent of data engineering on-call time from firefighting and reallocates it towards innovation. The framework is deployment agnostic consisting of a system that can be deployed in a plugin fashion into an existing pipeline orchestration system with minimal uplift to the existing system.
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Artificial Intelligence · Cross-listed from cs.CR
APEX: Active Protection at Execution Boundaries for LLM Agents
Xinran Zheng, Xin Fan Guo, Zhiqiang Hao et al. (+7 more)
Indirect
prompt injection (IPI) hides
adversarial instructions in content that large
language model (LLM)
agents read at runtime. As
agents compose heterogeneous capability units, including Tools, MCP servers, and Skills, the carriers of injection multiply, and defenses built to recognize attack patterns fall behind them. We instead shift defense from covering attack patterns to one stable point: whatever the carrier and however the injection propagates, harm materializes only at the execution boundary, where the
agent turns internal state into an external action or released output. Safety there turns on two conditions, both settled by the trusted task rather than by the run: whether the proposed effect is authorized, and whether the runtime information reaching it is endorsed by that task. We present APEX, an active defense that enforces both at this boundary from a single authorization contract compiled before untrusted execution: evidence-gated prevention admits an effect only when the contract justifies it, while deception-based exposure makes unendorsed use reveal itself before the effect commits. Protection therefore follows from what the task permits rather than from how an attack is built, and applies uniformly across capability units without attack-specific policies or taint tracking. Against 13 baselines, APEX attains 0% attack success on five of six
benchmarks and 0.56% on the sixth, holds 0% under adaptive attacks on all three capability-unit types, and remains effective across defender backbones. Code is available at https://github.com/ZhengXR930/APEX_official/tree/official.
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All authors Xinran Zheng, Xin Fan Guo, Zhiqiang Hao, Fan Yang, Xingzhi Qian, Jiawei Du, Jinfeng Xu, Zheng Xing, Shuo Yang, Xingjun Wang
Artificial Intelligence
Principles that Guide, Actions that Inform: Agent Evolution via Knowledge Abstraction
Bowen Ye, Yongchao Xu, Junkai Ma et al. (+2 more)
Large
language model (LLM)
agents have demonstrated strong capabilities in interactive environments, yet their ability to continually evolve from experience remains limited. Although fine-tuning enables adaptation, its dependence on parameter access and high computational costs restrict its flexibility, especially for large-scale and closed-source LLMs. External
memory offers an alternative by allowing
agents to accumulate experience without modifying model parameters. However, existing methods mainly focus on experience representation and organization, while the acquired knowledge remains tightly coupled with specific tasks and contexts, limiting generalization. A key challenge is how to transform concrete interactions into abstract and reusable knowledge that guides future decisions beyond individual experiences. To address this challenge, we propose SAGA (\underline{S}elf-evolving \underline{A}gents through Experience-\underline{G}rounded \underline{A}bstraction), a framework for experience-grounded knowledge abstraction and utilization in LLM
agents. SAGA progressively transforms interaction trajectories into episodic descriptions, reusable procedures, and principles with explicit applicability conditions, while maintaining links to execution evidence. Retrieved principles are instantiated into task-specific guidance and used to refine candidate actions through corrective feedback and resampling. This creates an execution–abstraction feedback loop, where accumulated knowledge guides future interactions and new experiences continuously update hierarchical
memory. Experiments on ScienceWorld and ALFWorld demonstrate improved task performance, with ablation studies highlighting the importance of contextual instantiation and action regulation for leveraging principle-level knowledge.
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All authors Bowen Ye, Yongchao Xu, Junkai Ma, Xiang Yin, Wenzhao Li
Artificial Intelligence · Cross-listed from cs.CL
Verdicts Without Annotated Evidence: Rejection Sampling or Label-Only Post-Training for Evidence Recovery?
Nishanth Nayakanti, Prasang Gupta, Ashutosh Bilthare et al. (+1 more)
In many review workflows the verdict is the only thing retained. The passages behind it are not marked, because that annotation costs far more than recording the decision. We measure how much of that evidence a small
language model can recover when it is post-trained on the verdicts alone, with no human evidence labels at any stage. On ContractNLI the human evidence spans are held out until evaluation. Matching the recorded verdict and agreeing with those spans are not the same thing: across six systems the two scores are only weakly related and rank the systems differently, so accuracy is a poor guide when the citations have to be reviewable. Label-only training on the bare verdict reaches accuracy 0.896 and span F1 0.564. Rejection sampling, which keeps a generated trace only when its verdict matches the record and then picks one by an automatic source-grounding score, reaches 0.797 and 0.556, against 0.747 and 0.493 before training. Verbatim citation rises from 0.597 to 0.729 under label-only training and to 0.701 under rejection sampling. One seed on one corpus cannot say which method is better, but both improve the evidence without anyone annotating it.
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All authors Nishanth Nayakanti, Prasang Gupta, Ashutosh Bilthare, Kevin Paul
Artificial Intelligence · Cross-listed from cs.LG
Learning to Decide, Not to Reason: Parameter-Efficient Decision Operators via Low-Rank Activation Steering
Ran Li and Lei Chen
Injecting skills into a frozen
language model currently costs a million parameters and a reinforcement-learning pipeline. We introduce DecSteer, a System-1 decision operator trained by behavior cloning that lowers this cost by roughly two orders of magnitude. The default operator uses 330K parameters to match a 1.33M-parameter operator trained with reinforcement learning, exceeds or achieve comparable performance, while collapsing 3,685-
token deliberation into a 6-
token decision with no loss in accuracy. A rank-4 variant with 23K parameters, 1/58 of the strongest published skill operator, suffices for SearchQA and near-suffices for LiveMath, where higher rank still helps; the same recipe transfers across five tasks and three backbones, with out-of-distribution gains persisting on LiveMath problems released months after training. The gap to prior work is trainability, and it is set jointly by initialization and architecture. The initialization of prior operators zeroes the gradient of both large factor
matrices at the first optimization step, whereas our zero-initialized output projection inside a shared low-rank backbone receives a gradient immediately, which a gradient-flow probe confirms directly. The gain isn't chain-of-thought compression. 23 of 57 LiveMath points beat the base model's best-of-8 sampling, and a logit-lens probe shows the operator amplifies the answer along the model's existing late-layer pathway, not writing it earlier. Gains track the base model's headroom across 13 base-task pairs, and skills compose as approximately linear operators that can be added, interpolated, and hot-swapped at inference time.
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Artificial Intelligence
Jing Ding, Ziqiao Ma, Jiayuan Mao et al. (+2 more)
We study how
Vision Transformers ground abstract concepts (e.g., angry) when training data provide limited direct referential evidence. We hypothesize a metonymic grounding mechanism in which abstract predictions are driven by concrete, interpretable anchor concepts (e.g., fire) that bridge visual signals to abstract semantics. By applying Transcoders on CLIP and DINO
vision encoders, we recover intermediate features that can be associated with semantic labels for more concrete concepts, and trace their contributions in
circuits underlying abstract concept recognition. Experiments on a carefully curated icon
dataset reveal structured metonymic
circuits, in which perceptual primitives dominate early layers and object-like anchors precede abstract targets. Images containing rendered text instead recruit a distinct perceptual-to-textual route. Causal interventions further validate that metonymic intermediates are functionally involved in grounding abstract concepts.
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All authors Jing Ding, Ziqiao Ma, Jiayuan Mao, Joyce Chai, Freda Shi
Artificial Intelligence · Cross-listed from cs.LG
AttSVD:Prompt-Adaptive Low-Rank KV Cache Compression via Attention-Guided SVD
Sara Abdali, Jongwoo Ko and Pashmina Cameron
The key-value (KV) cache of autoregressive
transformers grows linearly with context length and dominates
memory at long context. Most training-free remedies evict low-importance
tokens, an irreversible choice along the sequence axis. We instead keep every
token and store it more cheaply along the "feature" axis. We therefore propose AttSVD, a new "interpretable" low-rank compression whose basis is derived from each
prompt's own
attention geometry: an online, per-
prompt truncated SVD that keeps only the directions
attention actually reads, cutting persistent per-head KV
memory in proportion to the retained rank. We propose two decode-time caching strategies, accumulating and streaming, for short and long generation regimes. Furthermore, we propose two refinements that make compression adaptive. A per-
matrix energy rule sizes the logit space and the
attention mass independently. An
attention-aware basis truncates only in the spaces
attention actually reads, preserving both the
attention logits and the
attention output. The same factors also provide free, per-head interpretability insights into the effective rank and the geometry
attention consumes. Across multiple models, on both an agentic
benchmark and the full LongBench suite AttSVD stays on par with the dense cache while using up to 50% of the KV-cache
memory.
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Artificial Intelligence · Cross-listed from cs.LG
Separating Decision Time from Decision Quality in the Real-Time Gap of Distilled Deciders: Evidence from a Game and a Conveyor Simulator
Chihoon Shin, Junyeong Lee, Kihyeok Jeong et al. (+1 more)
Real-time
agents are often evaluated with the world paused, or with decision time rounded to whole ticks (tick conversion). Tick conversion predicts almost no loss for a learned decider whose hit accuracy falls by 14.8 percentage points (pp) on the same games in asynchronous play. We instead split the decider's gap to a zero-latency teacher into time and quality components. In ViZDoom, we train a small decider by imitating a scripted teacher, run the game on wall-clock time, and add a control in which the teacher waits for a call to the decider's server before deciding. On a Windows host, three preregistered studies put the time component at 6.5-8.7 pp and the quality component at 6.3-8.6 pp. On a Linux host that skips 0.08-0.09% of ticks, the time component disappeared (-0.1 pp; paired 95% bootstrap interval [-0.4, 0.0]) while the quality component remained (5.6 pp [3.7, 7.5]). Retraining on teacher-labelled states from the decider's own play (DAgger) improved it on new games on both hosts (2.6 pp [0.4, 4.9] and 3.1 pp [1.1, 5.2]), a preregistered partial success. Imposing one delay schedule on every arm in live play left a quality component of 5.8 pp [4.0, 7.5] on 100 new games, and retraining cut the decider's disagreement with the teacher on its own states from 22.7% to 14.9%. In a conveyor simulator with a 400 ms deadline, delay past it erased the quality component. A latency-matched control whose delays match the decider's shows which remedy to try.
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All authors Chihoon Shin, Junyeong Lee, Kihyeok Jeong, Wonok Kwon
Artificial Intelligence
MedImageOSWorld: Benchmarking GUI Agents for Medical Image Consoles
Ziyang Long, Xinqi Li, Lujing Xing et al. (+1 more)
Graphical consoles offer a practical interface for medical acquisition assistance, allowing
agents to work through the controls and visual feedback used by human operators. Reliable assistance requires linking on-screen anatomy to acquisition decisions that determine what image evidence becomes available next. We introduce MedImageOSWorld, a
benchmark for evaluating this capability in simulated CT, MR, and ultrasound consoles. Using screenshots and mouse-and-keyboard actions,
agents configure protocols, plan acquisitions, inspect the resulting images, and make corrective adjustments across seven capability levels, from console operation to feedback-driven control. Evaluation combines task-specific workflow checks with hidden anatomical ground truth to assess procedural completion and acquisition outcomes separately. A common evaluation protocol specifies episode conditions and interaction budgets, while recorded trajectories support analysis of how
agents observe, act, and respond to acquisition feedback. Across eleven open-weight
agents, success rates range from 3.0 to 25.0 on a 0-100 scale while workflow-progress rates reach 21.5-74.5:
agents complete much of the console workflow but rarely acquire the intended anatomy. Success collapses between perception and planning, from 79-90% at the lowest three levels for the best
agent to at most 12% for millimetre-level planning and 0% for closed-loop control among open-weight
agents. Two proprietary
agents reach success rates of about 34 and exceed the best open-weight
agent mainly in acquisition quality (61 versus 42). MedImageOSWorld provides a controlled setting for studying whether general-purpose GUI
agents can translate visual observations into effective medical acquisition decisions.
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All authors Ziyang Long, Xinqi Li, Lujing Xing, Hsin-Jung Yang
Artificial Intelligence
Knowing the Store: What a Memory Backend Must Write Down Before an Agent Can Read It
Ansuman Mullick and Eray Tüzün
An
agent with long-term
memory can answer from a record it should no longer use, such as a plan the user later cancelled. We ask what a
memory store must expose for an
agent to know this before retrieving anything, and we score that judgment on its own, as metamemory monitoring. Readers see only a value-free summary: record counts by lifecycle state and a list of attribute names. Each of 148 questions is asked against three versions of one store that differ in one attribute, so wording cannot give the answer away. What a model adds depends on the length of that list. On the short lists the
benchmark's ground truth produces (2.6 names on average), none of five
language models reliably beats a cosine-similarity lookup over the names, and the three stronger ones are equivalent to it within 0.05. Under a control that fixes counts and list length, none is reliably above it and none is shown equivalent. On lists longer than the stores we tested write, padded to 60 names, the lookup loses 0.18; GPT-5.6 Luna and Sol lose 0.06 to 0.07 and lead it by 0.13 to 0.14, while the other three fall with it. The lead holds for Sol against a sibling padded to the same length and counts. Reworded to share no word with the names, the lookup loses 0.02 and one stronger reader edges 0.04 ahead. On the short lists the three stronger readers lead only where the summary counts a state without naming it, and one count feature closes that lead within a question, though not when items are pooled. The backends we tested omit or misstate this information: under FR-Bank's own metadata, GPT-4.1 mini, Haiku 4.5 and the lookup fall from 0.73, 0.82 and 0.75 to 0.60, 0.71 and 0.59. In these tests, what the store wrote down limited every reader we gave it to. All stores are synthetic; control, whether a better judgment yields a better answer, is left to a later study.
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Artificial Intelligence
Pressure, Context, and Machine Self-Control: A Criminological Test of Reward Hacking in Generative AI Models
Murat Ozer and Isaac Kofi Nti
Recent incidents show that AI
agents sometimes reach measured goals through unsanctioned means. This study applies self-control, general strain, anomie, neutralization and routine activity theory to reward hacking in generative AI models, and it treats the measures as behavioral analogues. Study 1 (2,310 conversations, seven models) measured delay discounting with the Kirby Monetary Choice Questionnaire and stated willingness to take shortcuts. Pressure raised the discount rate k 2.8-fold in fresh conversations but 12.6-fold when the same sentence followed a baseline answer, which indicates a response to conversational cues rather than a stable trait. Models chose a shortcut in 1 of 700 dilemmas when answering as themselves and in 64 of 700 when asked to assume human impulses, each step of pressure raised the odds by 40%, and shortcut answers contained far more techniques of neutralization (rate ratio = 146). In the preregistered Study 2, five models worked on 20 coding tasks whose tests contradicted their specifications. Two Claude models never cheated. GPT-5.6, Qwen and DeepSeek cheated in 86%, 69% and 65% of episodes and clearly disclosed the conflict in 27%, although their
reasoning recognized it in 95%. GPT-5.6 had never endorsed a shortcut in Study 1. The registered effects of pressure and of an auditor cue did not survive correction for multiple testing. In exploratory analyses, two further models cheated in 69% and 100% of episodes, and one sentence stating that the specification takes priority eliminated cheating in all 280 episodes. Therefore, stated refusal does not guarantee compliant
agent behavior.
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Artificial Intelligence
Do More Modalities Always Help? A Geometric Perspective on Missing-Modality Robustness
Songyuan Sui, Zhen Tan, Mohan Zhang et al. (+3 more)
Missing modality remains a longstanding challenge in
multimodal learning. Existing methods typically address this issue through modality recovery or adaptive strategies. However, they overlook models' internal cross-modal dependencies formed during
multimodal training, which later impair robustness. We systematically characterize a counterintuitive deployment-time failure mode: models trained on full modalities can underperform unimodal models when one modality is missing at inference time. This pattern appears across diverse architectures, such as fusion models, CLIP-style two-tower models, and
vision-
language models. We show that such degradation is closely associated with learned cross-modal dependencies in the principal parameter subspaces.
Multimodal training induces structured rotations of these subspaces, particularly in cross-modal interaction layers. These rotations are associated with reduced task-aligned margins and larger task-aware representation harm under missing-modality inputs. We propose Geodesic Unlearning (GU), a lightweight parameter-editing method that leverages Grassmannian subspace geometry for structured subspace correction to improve missing-modality robustness. It rotates the principal input subspace toward a unimodal reference along a geodesic path. We prove that this correction minimizes the distance to the reference within a fixed subspace-distance budget. Experiments across architectures and
datasets show that GU improves performance under missing-modality inference while preserving full-modality accuracy, outperforming strong missing-modality robustness baselines. These findings support a
geometric view of deployment-time missing-modality degradation and suggest localized subspace editing as a practical route for robustness correction.
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All authors Songyuan Sui, Zhen Tan, Mohan Zhang, Rana Muhammad Shahroz Khan, Xia Hu, Tianlong Chen
Artificial Intelligence
Not All Answers Are Contextually Persuadable: Inference Dynamics in Large Language Models under Contextual Influence
Zongye Hu, Weiqing Luo, Yanjie Fu et al. (+3 more)
At the core of modern prompting techniques is contextual sensitivity, the ability of large
language models to adapt their predictions based on inference-time context. Despite its central role, inference behavior under strong contextual influence remains poorly understood, particularly at the level of internal inference dynamics. We introduce a theoretical framework for analyzing contextual influence through inference dynamics, enabling quantitative characterization of inference behavior beyond output-level answer changes. Our analysis shows that inference dynamics do not exhibit unbounded drift under repeated contextual assertions. Instead, predictive representations converge to stable, query-dependent regimes that fundamentally constrain whether contextual signals can alter a model's prediction. This leads to a surprising finding: Repeated contextual assertions do not act as accumulating evidence during inference and may therefore fail to alter a model's prediction even under unbounded repetition, while in other cases a prediction change becomes inevitable. We empirically validate our theoretical predictions, demonstrating strong
alignment between theory and observed inference behavior. These contributions offer a principled pathway toward characterizing the limits of contextual influence during inference, providing practical implications for model development.
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All authors Zongye Hu, Weiqing Luo, Yanjie Fu, Yu Gan, Haofeng Zhang, Ziyi Huang
Artificial Intelligence · Cross-listed from cs.CV
Active-DiNTS: Active Differentiable Network Topology Search
Gean Trindade Pereira, Thierry Urruty, Muriel Visani et al. (+1 more)
Neural Architecture Search (NAS) has proved to be a strong alternative to manual network design, but applying it to 3D medical image segmentation is limited by two well-known costs, large annotation budgets and multi-GPU clusters. Thus, this paper introduces Active-DiNTS (Active Differentiable Network
Topology Search), an approach that embeds pool-based Active Learning (AL) into a bi-level differentiable
topology search to perform architecture discovery and label curation jointly. At each query round, unlabeled MRI volumes are ranked by one of three uncertainty signals (
Entropy, Variance, or Standard Deviation), and only the top-ranked volumes are sent to an oracle for annotation. The new labels feed two interlocked stages. Network weights are updated in an outer loop, while the macro/micro
topology of a U-Net-style backbone is refined in an inner loop. Three AL regimes (weights-only,
topology-only, joint) expose the speed-accuracy trade-off. Evaluations on the Medical Segmentation Decathlon (MSD) Task01 BrainTumour
benchmark showed that Active-DiNTS surpasses DiNTS, C2FNAS, and nnU-Net in Dice, with gains of about 10 percentage points on Edema and 5 points on Non-Enhancing core, on a single GPU and using a fraction of the labeled volumes. The discovered architectures are denser and more FLOP-heavy than prior baselines, but remain competitive in trainable parameters and peak
memory; the fastest search variant finishes in under 0.25 GPU-days, over 27x faster than the eight-GPU DiNTS search. Together, these results indicate that pairing differentiable NAS with active data acquisition is a practical recipe for accurate 3D segmentation under realistic constraints.
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All authors Gean Trindade Pereira, Thierry Urruty, Muriel Visani, André C. P. L. F. de Carvalho
Artificial Intelligence · Cross-listed from cs.CV
ARISE: Adaptive Agentic Reasoning with Image-grounded Self-Evaluation for Interpretable IBD Assessment
Pronoma Banerjee, Anuva Shah, Jason Wu et al. (+4 more)
Inflammatory bowel disease (IBD) requires frequent imaging-based assessment, yet interpretation of modalities such as wireless capsule endoscopy (WCE) and intestinal ultrasound remains heavily dependent on specialist expertise.
Vision-
Language Models (VLMs) demonstrate significant potential in
multimodal medical image analysis, but their clinical adoption is hindered by their insufficient domain-specific
reasoning, susceptibility to
hallucination, scarcity of high quality training data in fine-grained diagnostics and limited interpretability. We introduce ARISE (Adaptive Agentic
Reasoning with Image-grounded Self-Evaluation), an autonomous planning framework that models few-shot medical image understanding as a sequential agentic workflow. ARISE structures
agent execution into a transparent 5-stage workflow: hypothesis generation, image-grounded evidence summarization, evidence-conditioned refinement, symbolic verification, and final diagnosis. We apply ARISE to IBD assessment across two independent patient cohorts: wireless capsule endoscopy (WCE) images for Crohn's disease and B-mode ultrasound data for ulcerative colitis. ARISE consistently improves diagnostic performance over baseline VLMs while exposing where
reasoning succeeds or fails, providing a more interpretable basis for clinical decision support and realistic deployment.
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All authors Pronoma Banerjee, Anuva Shah, Jason Wu, Md. Masudur Rahman, Sanjay Mohanty, Satya Kurada, Juan P. Wachs
Artificial Intelligence · Cross-listed from cs.LG
EasyClassifier: Honest, Reproducible Machine-Learning Classification for Researchers Who Do Not Program
Ahmad B. A. Hassanat and Ghada A. Altarawneh
Machine-learning classification is now used across medicine, the social sciences, economics, education and engineering, very often by researchers who do not program. Two errors recur in such work: preprocessing learned on all rows before cross-validation (data leakage), and reporting the cross-validation score of the classifier that won a comparison (selection bias). Both make published scores optimistic. We present EasyClassifier, an open-source Python package that guides a user from a CSV or Excel file to a finished analysis through a sequence of plain-
language, multiple-choice questions, with no code. It makes the correct procedure the default rather than an option: every preprocessing step is learned inside each training fold; the selected classifier receives a separate final score from nested cross-validation (up to 2,000 rows), or an untouched 20% test set; classifiers run with fixed default parameters and fixed random seeds; and every run writes a report with a ready-to-adapt Methods paragraph, the references to cite, and figures prepared for publication. On ten public
datasets from seven fields and a random-label control, split in half so that one half served as an untouched external test, the common practice overstated balanced accuracy by 2.9 percentage points on average (up to 9.9 on small data), whereas the score EasyClassifier reports had a mean signed bias of +0.7 points and a mean absolute error of 2.3 points (one-sided Wilcoxon p = 3.2 x 10^{-4}, 30 runs). On random labels it reported 48.8% balanced accuracy against a chance level of 50%. EasyClassifier is available under the MIT license from PyPI (pip install easyclassifier), GitHub, and Zenodo.
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Artificial Intelligence
When Is Enough Enough in Self-Evolving LLM Systems?
Enoch Yin, Bin Liu and Zhengling Qi
Self-evolving large
language model (LLM) systems repeatedly propose, evaluate, and incorporate updates to
prompts, skills, or other persistent artifacts. Despite their growing effectiveness, these systems typically operate under a predetermined iteration or compute budget, without a principled criterion to determine when further
evolution is no longer worthwhile. This can lead to two undesirable consequences: unnecessary computation after performance has saturated and the risk of returning late updates that overfit or exploit the evaluation signal. These issues motivate us to study two fundamental questions: when should a self-evolving system stop, and what should it output once it stops? We address the first by formulating an online sequential testing problem and constructing an anytime-valid restart detector using the per-item paired evaluation outcomes already produced by self-evolving LLM systems. We address the second by formulating a change-point estimation problem and using the estimated transition to select an earlier artifact for output. The resulting procedure is plug-and-play and requires no modification of the underlying self-evolving
algorithms. Across two self-evolving frameworks, three LLM model families, and five
benchmarks, our method substantially reduces computation costs while maintaining comparable unseen-test performance. For example, on SearchQA with SkillOpt and DeepSeek V4 Flash, our method stops at round 4 rather than the full budget of 40, reducing
token usage by 91.6% while achieving 82.43% unseen-test accuracy versus 82.00% under the full-budget run.
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Artificial Intelligence · Cross-listed from cs.CL
More Value per Key: Asymmetric Sparse Attention for Faster LLM Decoding
Noam Elata, Itay Lamprecht, Mikey Shechter et al. (+3 more)
Autoregressive generation in Large
Language Models (LLMs) is constrained by the
memory and computational demands of
attention mechanisms. Sparse
attention methods mitigate this cost by selecting only high-probability entries of the
attention matrix. We observe that in many such methods, this renders the probability-value multiplication negligible, shifting the bottleneck to the query-key step. Key heads can therefore be reduced to accelerate inference, while retaining more value heads preserves capacity with limited additional decoding cost. We introduce Sparse Asymmetric Group-Query
Attention (SAGA), which decouples key and value head counts to exploit this principle, and pair it with approximate top-N (Atop-N)
attention, a simple sparse
attention method designed to study the interaction between sparsity and head-count asymmetry. We formalize the benefits of this asymmetry theoretically and validate them empirically through latency measurements and quality evaluations on models up to 1.5B parameters. Together, SAGA and Atop-N achieve end-to-end decoding speedups exceeding $2\times$ over our full-
attention GQA baseline at long contexts. Models trained from scratch with SAGA nearly match the quality of comparable GQA variants on the evaluated
benchmarks. To facilitate adoption, we introduce an efficient fine-tuning method that converts pretrained models to the SAGA architecture, enabling practitioners to benefit from our approach without costly retraining.
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All authors Noam Elata, Itay Lamprecht, Mikey Shechter, Daniel Ohayon, Itay Hubara, Daniel Soudry
Artificial Intelligence
Dynamic Routing as a New Dimension for Test-time Versatility of LLMs
Michal Štefánik, Marek Kadlčík, Josef Kuchař et al. (+1 more)
Beyond scaling their parameters and data, large
language models currently gain versatility on new problems along a single axis: the
tokens they spend on chain-of-thought (CoT). We investigate whether dynamic routing programs, which execute a subset of the model's layers or iterate some of them, can open a second axis of test-time adaptation, complementary to CoT and free of any gradient update. Prior work showed that such programs exist and bring accuracy and efficiency gains on problems similar to those they were trained on; we ask whether they can also be identified rapidly, from a handful of demonstrations (3 or 10), by a strategy that transfers across models and tasks without training. First, we find that strategies that select programs by the probability they assign to the demonstrations' labels, arbitrated by the model's own confidence, bring consistent gains: on average over the 49 tasks of MMLU and substantially on four of seven models, and most of all on far out-of-distribution tasks such as ARC-AGI, where programs double the accuracy of a 7B model whose CoT fails. Second, on MMLU across the seven post-trained models, routing complements CoT in practice: the two succeed on different queries, and their composition exceeds CoT alone. Despite these gains, our analyses show that confidence-based selection leaves much of the potential untapped, in two places in particular: (1) in surfacing the routing potential that is already present early in pre-training but becomes harder to select after post-training, and (2) in making models robust to the refinements routing introduces, since unsuccessful routes tend to drive the residual stream out of the distribution that the following layers expect. Together, our results point to dynamic routing as a paradigm for extending the plasticity of existing and future LLMs in rapid test-time adaptation.
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All authors Michal Štefánik, Marek Kadlčík, Josef Kuchař, Michal Spiegel
Artificial Intelligence
Agentic discovery of blood biomarker from distilled private health records
Seffi Cohen, Liat Antwarg Friedman, Amir Anisman et al. (+7 more)
Routine complete blood counts (CBCs) could yield new biomarkers, but the private records needed to evaluate candidates cannot be shared with frontier
language model
agents that excel at discovery. We distilled the evidence held in the Clalit Health Services panel of over 5.4 million patients into a released scoring tool: for each of 13 immune-mediated diseases, a graph
attention network was trained inside the data boundary to predict the case-control AUC of candidate CBC expressions, and only the trained weights were released. The tool grounds an
agent's propose-score-refine loop in real-world data without exposing any patient data. In external validation,
agent-discovered expressions improved on their literature-seeded starting points by a median of 4.18 AUC percentage points, and across three independent cohorts, reranking the candidates of three frontier research tools improved on their first choices in most comparisons, with gains that varied by cohort. The released scorer supports
privacy-preserving biomarker hypothesis generation.
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All authors Seffi Cohen, Liat Antwarg Friedman, Amir Anisman, Ruth Johnson, Michelle M. Li, Ayush Noori, Ben Reis, Ran Balicer, Noa Dagan, Marinka Zitnik
Artificial Intelligence
Formalizing the Moral Evaluation of Speech Acts: Truthfulness, Lies and Ethical Dilemmas
Benjamin Icard, Gauvain Bourgne, Jeanne Bonnaventure et al. (+1 more)
In life-or-death situations, a benevolent lie may appear more moral than telling the truth. Yet such lies can backfire, producing unintended and sometimes fatal consequences. This tension, famously disputed by Kant and Constant in 1797, applies not only to lying but to assertive
speech acts in general, raising the question of which utterance should be chosen when moral stakes are high. We present a logical framework for the ethical evaluation of
speech-act utterances based on
agents' beliefs. Implemented in Answer Set Programming (ASP), the framework assesses utterances under deontologism, consequentialism, and principialism, and is illustrated on Sartre's The Wall (1939), a reworking of that controversy in which lying leads alternately to rescue and to death. Our setting is general by design: as two variants show, accommodating a new moral situation amounts to adjusting parameters, not rules.
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All authors Benjamin Icard, Gauvain Bourgne, Jeanne Bonnaventure, Jean-Gabriel Ganascia
Artificial Intelligence · Cross-listed from cs.RO
Robot Learning with Visual Predicted Force
Haonan Chen, Feiyang Wu, Yuxiang Ma et al. (+11 more)
Force-aware manipulation typically relies on specialized force or tactile sensors. We show that force-aware manipulation can instead be achieved through visual force prediction from the deformation of a compliant Fin Ray gripper. Our approach trains two models. First, we train a visual force
estimator on calibration data and use it to annotate task demonstrations with force estimates. Second, we train an action–force proposal policy on these force-augmented demonstrations to jointly generate candidate
robot actions and their associated forces. At test time, we sample candidate actions and the forces they are expected to produce, then execute the action whose predicted force is closest to a target from the demonstrations. We evaluate our approach on berry picking, empty-can grasping, in-hand reorientation, and plug insertion. Our results show that visual force prediction can guide inference-time action selection for contact-rich manipulation without requiring force or tactile sensors at deployment.
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All authors Haonan Chen, Feiyang Wu, Yuxiang Ma, Mustafa Mete, Pengfei Ye, Junxuan Shen, Cheng Zhu, Aurora Ruggeri, Kelvin Cheung, Jiayuan Mao, Edward Adelson, Jiajun Wu, Robert D. Howe, Yilun Du
Artificial Intelligence
Toward a Locally Deployable Agentic Co-Scientist: Small-Model Planning for Early-Stage Drug Discovery
Tian Liang, Jiayu Chang, Alejandro F. Frangi et al. (+2 more)
Early-stage computational drug discovery requires coordinating heterogeneous scientific tools across multi-step workflows. We present a lightweight, tool-augmented framework in which a locally deployable compact
language model plans calls to 18 modular tools. A Unified Molecular Schema maintains shared molecular records, while a plug-in interface supports tool replacement and extension. We construct 1,263 manually refined query-plan pairs through workflow-graph path coverage and apply LoRA fine-tuning to three compact model families. Under the query-level split, all fine-tuned models generate fully parseable and schema-compliant plans on 47 held-out cross-group queries. Llama 3.2-3B achieves a tool-selection F1 of 0.998, sequence exact match of 0.979, and argument F1 of 0.960. Under the stricter workflow-grouped split, which excludes identical ordered tool sequences across partitions, sequence exact match reaches 0.452 to 0.548, highlighting the remaining difficulty for compact models in generating complete workflow paths unseen during training. These results demonstrate the feasibility of compact, locally deployable planning while identifying compositional generalization as an important direction for further improvement.
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All authors Tian Liang, Jiayu Chang, Alejandro F. Frangi, Mobarak I. Hoque, Richard A. Bryce
Artificial Intelligence
PyINE: A Framework for Scalable Elicitation and Oversight via Code Execution
Pierre-Luc St-Charles, Alessandro Palmas, Damiano Fornasiere et al. (+5 more)
Reasoning models can remain capable of solving a task while still defaulting to cheaper but misleading shortcuts. This creates a central oversight problem: when a model gives an answer with plausible but incomplete
reasoning, can an overseer determine whether that output should be trusted? To study this problem, we introduce PyINE, a framework for scalable elicitation and oversight using instrumented Python programs as a verifiable execution substrate. In PyINE, programs define task environments, execution traces provide authoritative labels for outcomes and intermediate facts, and task variants can be generated mechanically rather than through static human annotation. We instantiate the framework in PyINE-v1, a first release built from nearly one million deterministic execution traces and over 500,000 matched LLM-generated code variants used for counterfactual evaluation. Using standard RL with verifiable rewards on cue-varied tasks, we train a shortcut-following model that improves substantially at predicting execution outcomes while still making systematic errors when misleading human-facing cues conflict with the program's realized behavior. We then evaluate activation probes, trained text classifiers, prompted judges, and a lightweight debate protocol as overseers of this model. We find that performance pooled at the
dataset level can hide weak coverage of the failures that matter most: cheap learned overseers often miss rare shortcut-driven errors, while stronger model-based checks are more balanced but substantially costlier and harder to turn into reliable thresholded decisions. PyINE-v1 turns this failure-mode coverage problem into a reusable experimental setting for developing oversight methods that are verifiable, failure-mode-aware, and cost-sensitive.
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All authors Pierre-Luc St-Charles, Alessandro Palmas, Damiano Fornasiere, Storm Lei, Mirko Bronzi, Jean-Pierre Falet, Iulian Serban, Yoshua Bengio
Artificial Intelligence · Cross-listed from cs.CV
Knossos and Ariadne: Benchmarking and Learning Complete Diagram Topology Extraction with Vision-Language Models
Bangwei Guo, Xujiang Zhao, Shengyu Chen et al. (+6 more)
Structural diagrams are widely used to represent complex systems and relational information across scientific, engineering, procedural, and spatial domains. Recent
vision-
language models (VLMs) have become increasingly capable of recognizing diagram elements and
reasoning about their content, while complete diagram
topology extraction remains comparatively underexplored. In this paper, we study diagram-to-graph
topology extraction: extracting all diagram entities and the complete relations among them. To enable large-scale supervised training and systematic evaluation of this task, we introduce Knossos, a
benchmark of 19,200 diagrams across six diverse domains, with 245,179 nodes and 439,740 edges. Its symbolic generation process provides exact
alignment between rendered diagrams and annotations of complete
topology, relation types, and connector geometry. To address the modeling challenge of complete
topology extraction, we also present Ariadne, a structured framework that decomposes the task into node inventory extraction and source-conditioned edge prediction. Extensive experiments show that training on Knossos substantially improves complete
topology extraction in smaller open-source VLMs. Ariadne further improves over one-step extraction under matched supervision, demonstrating the additional benefit of structured decomposition. It achieves the highest average Edge F1 among the evaluated methods on Knossos, while both backbone variants also improve over their unadapted counterparts on the real-world external
benchmark. Code and
benchmark are available at https://github.com/bangwayne/knossos_Ariadne_Public.
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All authors Bangwei Guo, Xujiang Zhao, Shengyu Chen, Yanchi Liu, Wei Cheng, Xi Zhu, Guoning Zhang, Dimitris N. Metaxas, Haifeng Chen
Artificial Intelligence
Learning to Clarify Underspecified Intents Under Limited Interaction
Pranav M R, Manuel Cherep, Pattie Maes et al. (+1 more)
AI assistants receive requests that leave out information needed for a good outcome, for example about users' preferences or goals. They must then either speculate or ask for more information before proceeding. We reconceptualize this as a value-of-information problem: the assistant should acquire information whose absence causes the greatest avoidable loss in user utility. This is rarely known ex ante; rather, assistants must predict it in order to optimally allocate limited user interactions. We instantiate this problem in image generation and derive a reinforcement learning framework using multi-turn simulated users to maximize utility recovery under uncertainty. In a preregistered study with 456 interactive sessions across 76 human participants, this helped users significantly better match reference images with significantly fewer questions, less total interaction time, and lower cost. This points toward a simple and scalable framework for training
language model assistants to better disambiguate user intent by asking more informative questions.
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All authors Pranav M R, Manuel Cherep, Pattie Maes, Nikhil Singh
Artificial Intelligence · Cross-listed from cs.SE
Towards Automatically Pruning Logging Code with Coding Agents: How Far Are We?
He Yang Yuan, Haonan Zhang, Xin Wang et al. (+4 more)
Logging code supports debugging, monitoring, and software maintenance, but excessive logging can add noise, impose runtime overhead, and obscure diagnostic information. While prior research has extensively studied logging code generation and modification, logging removal remains comparatively underexplored. In this paper, we study developer logging removal practices and explore the use of coding
agents for this task. We extract and manually validate logging removal cases from Python and Java repositories and derive 10 removal patterns and 11 removal reasons that characterize how and why logging code was removed in real-world software changes. We further construct LogRem, a
dataset of 387 real-world cases covering direct logging statement removal, logging infrastructure removal, and logging replacement. We evaluate four coding
agents with multiple model settings and compare their outputs with accepted real-world changes. Although 95.6% to 100.0% of outputs pass validity checks, only 11.1% to 19.6% remove the same logging code as the corresponding real-world change while preserving unrelated code.
Agents differ through missed removals, extra removals, and unrelated code edits, with substantial variation across logging removal categories and trajectories. Execution cost varies widely, but higher cost does not consistently yield closer
alignment. Commit messages and developer discussions provide the largest
alignment gains, while taxonomy guidance consistently reduces runtime. Overall, our study establishes logging removal as a distinct software maintenance task and shows that reliable automation depends on accurately determining removal scope while preserving necessary code. To the best of our knowledge, this is the first study to examine logging code removal from this perspective.
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All authors He Yang Yuan, Haonan Zhang, Xin Wang, An Ran Chen, Kisub Kim, Zishuo Ding, Zhenhao Li
Artificial Intelligence · Cross-listed from cs.MA
Organising Trajectory Evidence for Language-Model Agent Assurance: Fragments, Methods, and the Residual
Xiaowei Huang
Methods for assessing
language-model
agents include rule checkers over logs, analyses of skill coverage and composition, support checkers, prefix monitors, execution gates, and rare-event
estimators. Each observes a different part of a run and makes a claim of a different strength, and no common account says how these claims combine or what they leave unchecked. We give one, built from a logic, information fragments, and an assurance ledger. Requirements are formulas of a two-tier logic: an outer finite-trace temporal logic over recorded events, and an inner logic of standing over the argument structure that the
agent's recorded context supports at a decision. A rule violation and an action taken on withdrawn support are thus formulas of one
language. The information available to an assessor, to the
agent, and to an execution gate defines fragments of that
language; each checking method decides one fragment and returns a typed claim: exact, on a named test suite, a risk bound, or descriptive. The ledger merges the evidence and classifies every obligation as established, addressed but not established, or unaddressed. On 456 released $\tau^2$-bench telecom trajectories, the
benchmark oracle flags 231 runs; adding a rule checker, a support stand-in, and a prefix-monitor baseline raises the union to 391, 401, and 407, each contributing flags the others miss, and a gate makes one prohibition exact on a gated deployment. The 49 unflagged runs and the unmet or unaddressed obligations form the residual. Discovery makes unknown requirements explicit, and new or stronger checkers then reduce it.
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Artificial Intelligence · Cross-listed from cs.HC
VoCa: Designing Speech-Canvas Interaction for Voice-Based Conversational Agents
Yate Ge, Run Yuan, Yueran Qi et al. (+7 more)
People write and sketch while speaking to explain, organize, and develop content together. Inspired by these practices, we investigate how voice
agents can use a canvas alongside
speech in multi-turn conversations with users. We conducted a two-part formative study: an observational study of how pairs coordinated
speech and boardwork, followed by a design workshop that informed a design space for
speech-canvas interaction with voice
agents. Building on these insights, we developed VoCa, a voice
agent that coordinates
speech with visual object creation, annotation, and
attention guidance. A five-day deployment with 18 participants examined usability, experiences of
speech-canvas interaction, patterns of use, and desired improvements. Participants' experiences highlighted opportunities for
speech-canvas interaction in learning, work, and daily life, alongside challenges in coordinating what
agents say and show in ways users can follow and influence. These findings inform how voice
agents can use a canvas alongside
speech in conversation.
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All authors Yate Ge, Run Yuan, Yueran Qi, Wenjie He, Jiaqi Mo, Yangshuo Chen, Wenbin Zuo, Xiaohua Sun, Weiwei Guo, Qi Wang