Artificial Intelligence · Cross-listed from cs.RO
From Pixel to Poses: Object-centric Tool Manipulation Learning from Human Demonstrations
Bangjun Wang, Longyan Wu, Yukun Wei et al. (+8 more)
Scaling up robotic manipulation is primarily bottlenecked by the scarcity of real-world
robot data. While recent approaches leverage human
video demonstrations to mitigate this shortage, they remain computationally expensive and still rely on paired human-
robot data for domain
alignment. Although current state-of-the-arts excel at long-horizon tasks, they struggle with the delicate and precise control required for complex tool manipulation. To overcome these limitations, we introduce P2P-T, from Pixel to Poses for Tool Manipulation, a data-efficient, object-centric framework that learns tool use directly from human demonstrations. P2P-T bridges the cognitive and physical execution gap through a two-stage approach. First, pretraining an object-centric world model to extract stable pose priors; second, integrating these priors into an efficient, pose-aware low-level policy. By utilizing a robust automated data processing pipeline powered by modern foundation models, P2P-T completely bypasses the need for human-
robot aligned data. This reduces overall training overhead drastically. With minimal per-task fine-tuning, our framework achieves a 73% improvement over the previous state of the art in execution performance on complex, real-world tool manipulation tasks that currently remain out of reach for standard large-scale pretrained models.
Read the full abstract ▾
All authors Bangjun Wang, Longyan Wu, Yukun Wei, Shenghe Shao, Chaoyi Huang, Wenze Cui, Zetong Xu, Hanlin Wu, Long Chen, Yi Ma, Hongyang Li
Artificial Intelligence
A decision-support system applied to Law: Reasoning and explainability of the decision
Jeremy Bouche-Pillon, Pascale Zaraté, Yannick Chevalier et al. (+1 more)
The emergence of the digital transition brought an increasing need to control the processing of digital information, including in Law Enforcement Agencies (LEAs). At the EU level, in recent years, many regulations have emerged to control data processing and exchange. Texts other than the GDPR, such as the ”Law Enforcement Directive (LED)”, appeared to regulate specifically how Law Enforcement Agencies (LEAs) could process data. A formal representation of these regulations can be part of decision systems that support LEAs in processing data in compliance with the regulations. Although many new formalisms have emerged to represent legal norms and rules, few are provided with a
reasoning mechanism. Furthermore, systems used in decision-making processes in critical contexts such as medical diagnoses or legal decisions cannot be fully automated, and the explainability of their results is essential to ensure user confidence in decisions. This explainability aspect, while crucial, is lacking in most modern approaches that rely on machine learning. This paper describes a framework to operate formal rules from regulations, by focusing on explainability of the decision. After describing the general architecture of the proposed decision support framework, the paper showcases how symbolic AI and the SPARQL query
language can support legal
reasoning. It then describes an
algorithm to generate a justification for the
reasoning results, and outlines the procedure to be followed when the
reasoning does not lead to a satisfactory conclusion. We notably focus on a method based on decision trees to determine what additional information to request from the user.
Read the full abstract ▾
All authors Jeremy Bouche-Pillon, Pascale Zaraté, Yannick Chevalier, Nathalie Aussenac-Gilles
Artificial Intelligence · Cross-listed from cs.OS
Planarian: Managing Agent State with Statepoints
Jinnan Guo, Hao Mark Chen, Kapil Vaswani et al. (+2 more)
LLM
agents solve complex tasks by iteratively changing files, invoking local tools, and interacting with remote services, which modifies state across their local environment and remote services. Today,
agents and users must manage these changes explicitly, whether reverting exploratory actions or recovering from erroneous ones. Doing so safely requires coordinated actions, yet current
agent harnesses lack unified abstractions and mechanisms for managing local and remote state consistently and efficiently. We describe Planarian, an
agent runtime with state management that enables
agents and users to recover from erroneous actions and explore alternative executions over consistent local and remote environment state. Planarian introduces the abstraction of
agent statepoints, which are consistent, restorable point-in-time versions of the environment state. Planarian exposes three state-management primitives to
agents and users: (i) snapshot creates a new statepoint spanning local and remote state without requiring external services to support checkpoints: it relies on efficient incremental process and file system snapshotting to capture local sandboxed state, and transparently records compensating actions to undo remote state changes; (ii) rollback restores the environment to a previous statepoint by reverting to a prior local checkpoint and replaying compensating actions for remote state changes; and (iii) fork creates multiple isolated branches from a statepoint, enabling the
agent to explore alternatives in parallel. We show that Planarian enables
agents to undo mistakes and explore alternatives in parallel, improving task quality by up to 15x, and allows users to recover from erroneous actions with only 3% overhead.
Read the full abstract ▾
All authors Jinnan Guo, Hao Mark Chen, Kapil Vaswani, Andrew Paverd, Peter Pietzuch
Artificial Intelligence · Cross-listed from cs.LG
Do Temporal Link Predictors Need Learned Memory? A Smoothed-Count Baseline with a Handful of Parameters
Lisi Qarkaxhija and Ingo Scholtes
Many temporal link predictors summarize past interactions through learned node representations. We examine whether simple counts of recurring interaction patterns can provide competitive predictions without learning these representations. We propose a temporal link predictor based on statistical
language modelling. It pools transition and co-occurrence counts across sources to predict links that a source has never formed. We smooth sparse estimates using destination frequencies or Kneser-Ney continuation counts. A shared log-linear rule combines these estimates with popularity, source history, and recency, without node
embeddings. In our main evaluation, the model achieves the highest MRR among the compared methods on 7 out of 16
datasets from TGB and TGB-Seq. It also outperforms EdgeBank and Base3 on all 16
datasets and the heuristic family on 14. These gains extend to
datasets designed to limit repeated edges. With only 9–13 learned parameters, our model provides a simple and competitive baseline for evaluating future neural temporal link predictors.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.LG
Ruitao Liu, Qinghao Hu and Song Han
Large
language models (LLMs) typically generate text autoregressively (AR), predicting one
token at a time. Block
diffusion language models (dLLMs) instead generate blocks sequentially while denoising multiple
tokens in parallel within each block, offering a promising way to accelerate generation. Rather than training such models from scratch, recent work adapts strong pretrained AR models into block dLLMs through
distillation. On-policy
distillation (OPD) has been widely used for LLM training because it supervises the student on states generated by its current policy, rather than only on fixed offline trajectories. By training on the states the student actually visits, it reduces the mismatch between training and generation and can provide more relevant supervision as the student evolves. Recent work has extended this idea to AR-to-block-
diffusion conversion. However, this setting introduces a fundamental mismatch in supervision: the block-
diffusion student and the causal AR teacher condition on different information at the same training state. The student predicts from the entire partially denoised block, including visible future context, whereas the standard AR teacher target is defined only from the causal prefix. As a result, the teacher distribution used for
distillation is not fully aligned with the information available to the student. We therefore introduce d-OPD, a future-aware on-policy
distillation method that corrects the AR teacher distribution to better align with the student-visible state by incorporating visible future information within each block, providing supervision that better matches the information used by the student. Across Qwen3 models from 0.6B to 8B, d-OPD improves the six-
benchmark average by up to $4.0$ points over OPDLM and reduces training time by $1.35$-$1.58\times$. The code is available at https://github.com/mit-han-lab/d-OPD.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.SE
Do Coding Agents Reuse Existing Code or Reinvent the Wheel?
Dongsheng Ma, Sizhe Wang, Xinyi Huang et al. (+5 more)
Coding
agents are increasingly deployed for iterative development on real repositories, yet existing evaluation barely answers a basic question: do coding
agents reuse existing code or reinvent the wheel? The question matters: every duplicated implementation is a fix applied twice and
agents produce code far faster than humans can audit, so redundancy accumulates unsupervised. Thus, we present RepoReuse, a multi-turn
benchmark for
auditing code reuse in real repositories, where requirements are revealed turn by turn and the workspace accumulates across turns. It is built by a fully automated pipeline combining AST-based dependency graphs, guided evidence collection, and execution-verified task synthesis, and scales readily to new repositories. Beyond pass rates, we measure the reuse rate together with recall and cross-turn structural redundancy. An audit over 3{,}000 turns shows that
agents progressively stop exploring relevant repository code, reuse their own history less even when it is fully in the workspace, and leave duplicated logic in 50.8% of task chains by turn~5—all while pass rates barely move. Such deficiencies are invisible to pass rates, underscoring the need to evaluate code generation beyond functional correctness.
Read the full abstract ▾
All authors Dongsheng Ma, Sizhe Wang, Xinyi Huang, Zhengren Wang, Yuhan Wang, Luyang Si, Xincheng Wei, Wentao Zhang
Artificial Intelligence
Don't Inoculate Everything: Stratified Inoculation Prompting Narrows Backdoor Triggers and Preserves Desired Traits
Kajetan Dymkiewicz, Tim Farrelly, Adam Prada et al. (+7 more)
Supervised fine-tuning can teach
language models undesired behaviours alongside desired ones. Inoculation prompting (IP) aims to limit unwanted generalisation by requesting the undesired behaviour during training and removing the request at inference. However, undesired behaviour can still appear under unrelated
prompts. IP can also hinder learning of the desired behaviour. We address these limitations in settings where both behaviours co-occur in most training examples, so filtering out examples with undesired behaviour leaves only a small clean subset. We introduce stratified inoculation prompting (SIP). SIP leverages a small clean subset to demonstrate that desired behaviour should persist without the undesired one across different contexts. SIP oversamples these clean examples under diverse non-eliciting
prompts while inoculating the rest. SIP substantially reduces expression of undesired behaviour while preserving more of the desired behaviour than IP. These gains persist even when we extend IP to oversample the same clean subset at the same rate as SIP. Moreover, SIP yields lower emergent misalignment rates in all harmful-advice setups we tested. SIP can be further extended to limit the undesired behaviour even under
prompts that explicitly request it. We introduce
backdoor dilution, which weakens expression under the inoculation
prompt, and password-locked inoculation, which concentrates elicitation on a designated password. Taken together, our findings show that changing the training contexts for a small clean subset can significantly improve selective generalisation.
Read the full abstract ▾
All authors Kajetan Dymkiewicz, Tim Farrelly, Adam Prada, Ishaan Panigrahi, Srishti Gureja, Helen Yannakoudakis, Robert Mullins, Victor Gillioz, Daniel Tan, Maxime Riché
Artificial Intelligence
Jailbreaks for Black-Box Uncertainty Quantification in Large Reasoning Models
Lucas Biechy, Cédric Eichler, Adrien Boiret et al. (+1 more)
While Large
Reasoning Models (LRMs) excel at complex
reasoning,
alignment through reinforcement learning often induces systemic overconfidence. In production environments, where logits may be unavailable, robust black-box uncertainty quantification (UQ) is essential for trustworthiness and safety. Focusing on question-answering for LRMs, we show that existing black-box methods, such as paraphrase-based self-consistency and confidence verbalization, offer little to no improvement over simple repeated sampling, suggesting that
alignment suppresses useful output variability. We introduce
prompt-level relaxation operators that broaden the model's effective output distribution by approximating the effect of an optimal policy obtained with a stronger KL-regularization parameter, hence closer to the reference model. Theoretically, we demonstrate that relaxation improves calibration. We propose Jailbreak for Uncertainty (J4U), a jailbreak-derived technique for UQ that empirically reproduces the behavioral signatures predicted by our relaxation theory. Across 3
datasets and 4 LRMs, including a closed-source production model, J4U's improvement over repeated sampling achieves statistical significance in up to 6 times more LRM-
dataset-metric settings than the strongest black-box UQ state-of-the-art baseline we evaluate, with average ECE reductions up to 5 times larger. These results provide a practical tool for UQ in black-box LRM deployment.
Read the full abstract ▾
All authors Lucas Biechy, Cédric Eichler, Adrien Boiret, Nicolas Anciaux
Artificial Intelligence · Cross-listed from cs.LG
From Data to Program: Fast & Direct Generative Program Inference from Empirical Data
Simon Klüttermann, Xueying Ding and Leman Akoglu
Estimating probability densities from a finite set of samples typically requires
dataset-specific model fitting. We introduce PRODiGI, a pretrained data-to-program model that infers an explicit, executable generative program in a single forward pass. Pretrained on synthetic
datasets paired with their ground-truth programs, PRODiGI accommodates diverse generative families and data dimensionalities through template prediction and non-autoregressive program parameter decoding. Its inferred programs support direct sampling, density and score evaluation, and inspection independently of the pretrained model. We further introduce program-space fine-tuning, which refines differentiable program parameters by matching generated and empirical samples while keeping model parameters intact. Experiments show that PRODiGI achieves lower average density and score MAE than existing pretrained models, while offering multi-fold speedups over its closest competitors. Program-space fine-tuning further reduces generation MMD by 84%. By turning empirical data into explicit, reusable programs, PRODiGI introduces a new direction for fast, interpretable tabular generative modeling.
Read the full abstract ▾
Artificial Intelligence
Jev thinks "I don't know”, but doesn't say it: Introducing Sys1Cal-v1 Dataset for Probability Calibration
Riccardo Porcedda
The appearance of Jev marked the era of System One Models, foundation models that return structured decisions with probability distributions rather than text. Aside from low cost and great speed, Jev's central promise is that these probabilities are calibrated: such claim is not backed by any public test and available external
benchmarks evaluate confidence calibration, not whether every returned option probability has the right numerical meaning. To tackle this issue, we introduce Sys1Cal-v1, a
dataset of True/False questions about a proposition $A$ for which the exact probability $P(A)$ is known by construction. Each item is queried through the three Jev primitives - Noul, Choice and Score - and evaluated by total variation distance from the ground-truth distribution, which can be used to estimate a soft accuracy of System One Models. We showcase the utility of Sys1Cal-v1 as a
benchmark dataset by evaluating Jev and SemIf, an open-source Choice-style baseline. In this work, however, we focus even more deeply on Jev, by studying the calibration of its Score and Choice answers. In particular, we discover a peculiar behaviour that can be explained by assuming that Jev suppresses a third truth value, going beyond True and False. In other words, in Choice answers, $P(A)$ and $P(\neg A)$ are presented as if $P(A)+P(\neg A)=1$, while a term $P(U)\neq0$ is missing in the sum. Recovering $P(U)$ leads to an improvement of median soft accuracy in Choice answers from $0.771$ to $0.978$, suggesting that, even in binary decisions, Jev wants to answer with a third option:“I don't know”.
Read the full abstract ▾
Artificial Intelligence
TMCS: Tool-Grounded Multi-Agent Reasoning for Compositional Chemical Problem Solving
Shengqin Wang, Jie Jin, Yu Cheng et al. (+4 more)
Despite the promise of Large
Language Models (LLMs) in computational chemistry, rigorous combinatorial chemistry problems remain difficult because they require quantitatively constrained molecular modification, candidate validation, and systematic revision after failed attempts. Existing tool-augmented chemical
agents demonstrate useful planning and tool use, but they rarely provide a unified loop for property-driven molecular optimization and workflow-level composition. To bridge this gap, we propose Tool-Grounded Multi-
Agent Reasoning for Compositional Chemical Problem Solving (TMCS), a step-by-step multi-
agent framework that formalizes chemical problem solving as an interpretable, tool-augmented workflow. At the task level, specialized
agents leverage external tools, few-shot trajectory
memory, and structured reflection to iteratively refine solutions. At the workflow level, TMCS chains generation, understanding, editing, description, and optimization into a closed-loop pipeline. Evaluations across multiple chemical tasks demonstrate that TMCS consistently enhances chemical
reasoning across both open- and closed-source base models, achieving state-of-the-art performance.
Read the full abstract ▾
All authors Shengqin Wang, Jie Jin, Yu Cheng, Yihang Chen, Weilin Luo, Yuan Xie, Zhizhong Zhang
Artificial Intelligence · Cross-listed from cs.LG
Large Language Models for Automated Cross-Domain Machine Learning Task Type Identification: A Benchmark Dataset and Evaluation
Petros Tsialis, Steffen Limmer, Tobias Rodemann et al. (+1 more)
Machine learning task type identification is essential for constructing valid ML pipelines, yet in practice it is typically specified manually. We investigate whether large
language models (LLMs) can infer both the data domain and the downstream prediction task directly from
dataset-level information when only the target feature is provided by the user. Together with our LLM-based system we also release an annotated
benchmark comprising 625 public tabular and time series
datasets. We evaluate the proposed approach in three settings: (i) tabular
datasets in comparison with established AutoML heuristics, (ii) cross-domain evaluation across tabular and time series
datasets, and (iii) a practical deployment scenario using smaller local models. The results show consistent advantages for LLM-based task type identification, with increasing difficulty in heterogeneous and resource-constrained settings. LLM-based approaches outperform AutoGluon in the tabular setting, reaching 0.98 F1 macro compared to 0.93. In the cross-domain setting, the best model achieves 0.90 F1 macro, while smaller locally deployable models reach 0.75, indicating a trade-off between deployment feasibility and accuracy.
Read the full abstract ▾
All authors Petros Tsialis, Steffen Limmer, Tobias Rodemann, Martin Heckmann
Artificial Intelligence · Cross-listed from cs.GT
Reverse Sequential Proportional Approval Voting Rule: Proportionality and Approximation Guarantees
Georgios Papasotiropoulos
We study the Reverse Sequential Proportional Approval Voting Rule (RevSeqPAV) in approval-based committee elections. Despite its historical prominence and practical use, its properties and guarantees are much less understood than those of Sequential PAV. We analyze it along two dimensions: proportional representation (measured by Extended Justified Representation, its approximations, and proportionality degree) and approximation of the maximum PAV score of instances. We first establish strong negative results for general, unrestricted election instances and then identify settings in which the rule provides meaningful
fairness and optimization guarantees.
Read the full abstract ▾
Artificial Intelligence
Hyper Algorithm Design Agent: Evolving Learnable Optimizer from Zero
Zipei Yu, Yue-Jiao Gong, Zeyuan Ma et al. (+2 more)
Meta-Black-Box Optimization (MetaBBO) is one of the highlights in the recent AI for Optimization trend. This paradigm's bi-level workflow leverages the learnable
algorithm design policy at meta level to ensure the performance and generalization improvement on the low-level optimization task. While MetaBBO helps advance the performance lower bound of the resulted optimization system, it is currently handcrafted and customized case by case to adapt different optimization problems, which inevitably introduces inherent subjectivity and hence restricts the performance upper bound and usability in practice. In this paper, we address this issue by regarding MetaBBO's design loop as coding task, where we could introduce openendedness into MetaBBO with recursive self-improvement capability of advanced coding
agents. Specifically, we propose a dual-
agent framework: i) a task
agent continuously refines the codebase of a target MetaBBO approach through code
evolution; ii) a hyper
agent progressively modifies the task
agent and itself to provide open-ended design behavior; iii) the evolved MetaBBO codebase is evaluated and all in-execution information is fed back to the
agents for recursive self-referential improvement. As a result, given a naive MetaBBO template, our framework automates a design
evolution and finds novel variants superior to up-to-date human-made MetaBBO baselines. Surprisingly, the experimental results also demonstrate that our framework supports fast adaption across different optimization domains. Solid interpretation analysis further reveals interesting design principles emerge in such open-ended process. This work serves as the first exploration on automating design of complex learning-assisted optimization
algorithms.
Read the full abstract ▾
All authors Zipei Yu, Yue-Jiao Gong, Zeyuan Ma, Yuncheng Jiang, Zhiguang Cao
Artificial Intelligence · Cross-listed from cs.LG
Teacher-Student Gaps Are Not Enough: Outcome-Guided On-Policy Distillation for Multi-Turn Autonomous Agents
Tong Zhang, Zhou Liu, Yihao Liu et al. (+8 more)
On-policy
distillation (OPD) trains a student on its own trajectories with dense teacher supervision. Recent work on OPD for multi-turn autonomous
agents often treats large teacher-student
token-level distributional gaps as promising intervention points, linking larger gaps to a greater need for correction. Yet, our empirical analysis reveals a supervision-benefit mismatch: large gaps can be benign, while small gaps can be outcome-critical. Teacher-student gaps capture differences at the current turn, whereas the benefit of teacher guidance depends on how the current student interacts with the environment afterward. The student may still succeed despite choosing an action that differs from the teacher's, while a teacher-preferred action may lead to a state from which the student cannot complete the task. Local gaps alone are therefore not enough to determine whether teacher guidance benefits the current student. Effective supervision should instead emphasize guidance that the current student can translate into better final task outcomes. Accordingly, we propose Outcome-Guided On-Policy
Distillation (OG-OPD), which applies trajectory-relative weighting to teacher supervision and calibrates these weights using final task outcomes from paired student continuations. This calibration selectively strengthens supervision on the student's original trajectories at turns where teacher guidance benefits the current student. Across ALFWorld, ScienceWorld, and WebShop, OG-OPD consistently outperforms baselines under diverse settings. It improves task success rates by 3.6-17.7 percentage points over vanilla OPD and by up to 7.0 percentage points over the strongest baseline.
Read the full abstract ▾
All authors Tong Zhang, Zhou Liu, Yihao Liu, Jiahua Bao, Xuchen Li, Honglin Lin, Tao Cheng, Zhihan Yu, Kai Tang, Xiaoxi Jiang, Guanjun Jiang
Artificial Intelligence
Reliability Engineering for AI Systems: Challenges, Methods, and Directions
Rong Pan, Yili Hong and Min Xie
AI reliability concerns whether an AI system performs its intended function dependably over a stated period and under stated operating conditions, with stated evidence. As these systems become more autonomous, that function includes more than a correct output.
Retrieval,
memory, tool use, permissions, human oversight, and interactions among systems must operate consistently and safely, and, for generative systems, so must the
reasoning process that produces the output. Average
benchmark accuracy measures capability; it does not quantify this broader reliability claim. This paper adapts established reliability engineering methods, from failure definitions and operational envelopes to FMEA, accelerated testing, field monitoring, and reliability growth, to AI systems. A four-level diagnostic framework classifies failures as component, operational-loop, agentic-conduct, or network and governance failures. Test, evaluation, verification, and validation (TEVV), sequential monitoring, and FRACAS create and refresh evidence. SMART provides statistical guidance for measurement, analysis, assessment, and test planning; the NIST AI Risk Management Framework provides organizational guidance for governance, evaluation, monitoring, and mitigation. Three cases illustrate the program:
adversarial testing of a convolutional neural network, perception-error propagation, and autonomous-vehicle disengagements. Established reliability engineering provides a usable foundation; new measurements and safety guardrails are still needed as these systems are self-evolving.
Read the full abstract ▾
Artificial Intelligence
Narrowing the Horizon: Quantifying Topic Saliency Shifts in Generative Monoculture
Oriane Peter, Elena Simperl and Kate Devlin
As Large
Language Models (LLMs) become central to how we access and share information, they play an increasingly powerful role in shaping global knowledge. However, as these models evolve, their outputs risk converging into a generative monoculture, where the diversity of perspectives they represent narrows over time. Studies at the model level often fail to pinpoint which specific topics or viewpoints are being marginalised or amplified in this process. In this paper, we introduce a method to measure shifts in topic saliency across model families, tracking what gains or loses prominence during post-training. Applying this approach to a case study of
climate change discourse, we demonstrate how homogenisation affects the representation of diverse solutions across different models. We also test interventions to counter this trend, showing that specialised models can help preserve a broader range of perspectives. This underscores the importance of monitoring topic saliency to diagnose the risks of monoculture and to ensure AI systems reflect a pluralism of ideas. Data and Code are accessible \href{https://github.com/oriane/topic_saliency_shift}{here}.
Read the full abstract ▾
Artificial Intelligence
Training-Free Clinical Reasoning through Medical Ontologies and Cognitive Mapping: A Symbolic-Probabilistic Knowledge Graph Framework
Surajit Das
Most clinical prediction systems learn patient-variable-outcome associations; we investigate a training-free diagnostic paradigm mapping patient observations to explicit medical knowledge. CKG Reasoner integrates candidate-specific Evidence Feature Nodes, patient-reference matching, a bounded Information Gate, knowledge-weighted evidence accumulation, disease similarity, and decisive clinical rules. Missing-aware normalization and coverage
auditing distinguish absent from unavailable evidence. Candidate ranking is separate from outcome-label-independent K-means clustering, which uses four derived evidence coordinates (evidence strength, relative magnitude, directional similarity, and evidence completeness), not raw predictors or targets, to derive cohort-level assignments. Across six retrospective cohorts - four dengue (N = 1000, 1523, 989, 1018), malaria (N = 2190), and influenza (N = 4569) - a uniform, label-free, cohort-fitted K = 2 protocol yielded positive-class F1 scores of 0.996, 0.634, 0.936, 0.917, 0.695, and 0.842, and all-record accuracies of 0.996, 0.558, 0.914, 0.893, 0.707, and 0.906, respectively, with full partition-decision coverage using the frozen package and disease-specific knowledge representations. Neither scoring nor clustering uses outcome labels. Logistic regression provides a supervised baseline. Influenza incorporates confirmatory molecular PCR and is not independent pre-test prediction. Results characterize knowledge-grounded evidence separation, auditability, and sensitivity, not prospective clinical validity or comparative superiority. FOL/LLM-based clinical explanation remains unevaluated.
Read the full abstract ▾
Artificial Intelligence
AbGaze: Attentive Geometric Representation Learning for End-to-End Antibody Design
Jiashuo Wang, Siqi Fan, Yizhen Luo et al. (+1 more)
Computational antibody design requires representations that capture the
geometric patterns underlying antigen–antibody interactions, yet existing approaches often rely on scalar distances or surface-intrinsic features, leaving cross-molecular geometry largely implicit. We present AbGaze, an end-to-end antibody design framework based on attentive
geometric representation learning, which encodes distance, spatial direction, and surface-normal orientation of antigen surfaces relative to antibody-residue local frames, and adaptively aggregates these
geometric interactions according to their interfacial context. The learned interaction representation is shared across multi-CDR co-design, complex structure prediction, and affinity optimization, with local-frame
geometric supervision further constraining the representation. AbGaze outperforms prior methods across all three tasks: relative to the second-best method, it improves amino-acid recovery by 7.1% and reduces structural error by 14.9% on average over the six CDRs, improves interface docking quality (DockQ) by 6.6%, and raises the affinity improvement rate (IMP) by 32.5%.
Read the full abstract ▾
All authors Jiashuo Wang, Siqi Fan, Yizhen Luo, Zaiqing Nie
Artificial Intelligence
EvoIn: Bridging Evolution and Internalization for Agent Fine-Tuning
Shihan Dou, Shaofan Liu, Zhonghang Lu et al. (+8 more)
Recent work has explored improving
agents by jointly evolving their harnesses and models, but often takes a ”potpourri” approach that bundles together new tools, new decision-making procedures, and model adaptation to the evolved harness under a single notion of
agent improvement. In this paper, we instead investigate how
agents can improve their decision-making procedures. In particular, we propose EvoIn, an
agent fine-tuning framework that bridges
evolution and internalization. EvoIn first analyzes
agent execution traces to evolve and validate new decision-making procedures by temporarily instantiating them in the harness. The validated procedures guide the
agent to generate improved
reasoning traces. These traces are then rewritten into self-contained
reasoning traces, removing explicit references to harness instructions while expressing the induced decision logic as the model's own
reasoning. Finally, EvoIn fine-tunes the model on the rewritten traces, internalizing these procedures so that the improved decision-making persists without the evolved harness at inference time. We evaluate EvoIn on diverse
benchmarks and find that it consistently enables
agents to learn stronger decision-making procedures, raising the pass rate by 10.9 points in-domain and by 9.2 points out-of-domain. Results further show that the internalized decision procedures generalize to unseen tasks. Case studies show that
agents can learn to decide how to solve a task before solving it, for example by checking a document's length to choose between reading it in full and searching it. EvoIn is also broadly applicable, showing consistent improvements on another model family.
Read the full abstract ▾
All authors Shihan Dou, Shaofan Liu, Zhonghang Lu, Jiahang Lin, Shichun Liu, Binghai Wang, Jiajie Jin, Guanting Dong, Tao Gui, Qi Zhang, Xuanjing Huang
Artificial Intelligence
The Argument and the Letterhead: Source-Position Coherence in AI Evaluation
Michele Loi
An argument can be surprising coming from a particular speaker without being a bad argument. Do AI evaluators keep these judgments apart? Two preregistered descriptive studies and a later Jev supplement collected 2,976 usable evaluations of six fixed texts about US AI policy, Germany's debt brake and Swiss
nuclear energy. Each text was presented under several source attributions. The key comparison asks whether the gap between two sources changes when the argument changes. On Sol, for example, a national-security argument received mean ratings of 0.359 under CODEPINK and 0.639 under College Republicans; a civil-rights argument received 0.742 and 0.721. A constant preference for one source cannot explain that pattern. Related interactions appeared across topics and recent model configurations, including those with
reasoning enabled, while several comparisons yielded small effects. The later European Jev supplement yielded five interactions below the adopted absolute reference of 0.05; its distinct rubric and interrupted collection limit comparison with the chat systems. Some written evaluations explicitly invoked a mismatch between a source and its attributed position. Taken together, the numerical and verbal evidence supports source-position coherence as a plausible explanation, alongside competing accounts involving credibility, authenticity and interpretation of the task. The paper develops this inference through controlled comparisons, reports conditional post hoc p-values in an appendix, and documents the human decisions and delegated checks behind an AI-conducted study.
Read the full abstract ▾
Artificial Intelligence
Textual User Taste: Natural-Language User Context for Foundation-Model Recommender System at Scale
Ghazal Fazelnia, Paul Gigioli, Eliza Klyce et al. (+16 more)
Foundation model recommender systems require user context that can be consumed by large
language models, reasoned over, and refined through natural-
language interaction. Traditional behavioral
embedding vectors remain highly effective for
retrieval and ranking, but they are opaque to users and not natively expressed for
language model workflows. We present Textual User Taste, a system that generates structured natural-
language taste profiles from listening behavior, interaction signals, content metadata, and optional user feedback, and deploys them to millions of Spotify users. We describe the end-to-end production lifecycle required to generate, evaluate, optimize, and maintain these representations at industrial scale, including
prompt development and compression, user steering, and integration with downstream personalization systems. Because no unique ground-truth taste profile exists, we introduce a multi-faceted evaluation framework to evaluate taste profiles as a production representation: they carry user-specific predictive signal independently, and when integrated with behavioral
embeddings, improve MRR by 0.6% for future-track prediction and NDCG@7 by 2.2% for search ranking. Our evaluation also reveals that taste profiles support positive natural-
language steering, while exposing important limitations, including challenges with negation and short-term temporal adaptation. These findings position taste profiles not as replacements for behavioral
embeddings, but as an interpretable and steerable interface between evolving user context and foundation-model recommender systems.
Read the full abstract ▾
All authors Ghazal Fazelnia, Paul Gigioli, Eliza Klyce, Sharon Zheng, Katie Zelvin, Ye Myat Thein, Anurag Deshpande, Seda Davtyan, Kate Remeika, Maya Hristakeva, Erik Franco, Karen Banzon, Peng Ge, Jacqueline Wood, Nandini Singh, David Murgatroyd, Mounia Lalmas, Yves Raimond, Andreas Damianou
Artificial Intelligence · Cross-listed from cs.LG
Multi-Attractor GNNs: Set-Valued Expressivity Beyond Unique Equilibria
Jialin Liu
Recurrent and
equilibrium graph neural networks (GNNs) often enforce a unique fixed point or use one training target per graph. Yet many combinatorial and scientific problems admit multiple valid solutions, with no preferred one. A designated target can then impose an arbitrary selection rule. For tasks invariant to node relabeling, a symmetric graph may have a symmetric solution set but no symmetric solution. We show that multiple
equilibria enable one weight-tied message-passing GNN to represent set-valued equivariant maps: different initializations approach different valid solutions. Under stated regularity assumptions, we first construct globally Lipschitz, permutation-equivariant dynamics that converge almost surely to valid solutions and reach every solution branch with positive probability. We then establish approximate realization by recurrent message passing with continuous component maps, with arbitrarily small update and limiting errors and arbitrarily high probability. This goes beyond standard universality arguments: although message passing alone cannot distinguish symmetric nodes, the evolving state keeps nodes distinguishable at every finite step without auxiliary node identifiers. Such dynamics can be learned without solution labels using problem-specific energies. On Ising ground states, structural module detection in
protein graphs, and chemical reaction steady states, the learned updates produce multiple high-quality predictions with high numerical convergence rates. They achieve better average solution quality than the tested unique-
equilibrium, single-target, and feedforward baselines, while remaining competitive with much larger
diffusion-based solvers.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.LG
eval-unlearn: Benchmarking unlearning in Text-to-Image Diffusion Models
Mansi, Nikhil Raghavan, Zixia Huang et al. (+4 more)
The rising number of concept unlearning techniques for text-to-image (T2I)
diffusion models has produced a fragmented evaluation landscape. Methods are assessed under heterogeneous experimental conditions making principled cross-method comparison difficult. We present eval-unlearn, an open-source Python library providing a unified, reproducible benchmarking framework for concept unlearning in T2I
Diffusion models. eval-unlearn integrates twelve published unlearning techniques spanning fine-tuning, closed-form model editing, and inference-time intervention, alongside nine complementary evaluation metrics covering erasure efficacy,
adversarial robustness, generative quality, and concept retention. Its plugin architecture lets third-party techniques and metrics self-register without modifying the core framework, and its streaming, batched pipeline supports efficient evaluation of both standard NSFW concepts and arbitrary general concepts. As a further contribution, we release a public leaderboard on HuggingFace along with an interactive tool for real-time evaluation of unlearning techniques. The leaderboard compares nudity concept erasure case study across all twelve techniques, exposing significant accuracy-quality trade-offs that are obscured by heterogeneous evaluation. eval-unlearn is released under the MIT license; the package, code, leaderboard, and documentation are all available at https://eval-unlearn.readthedocs.io.
Read the full abstract ▾
All authors Mansi, Nikhil Raghavan, Zixia Huang, Kai Sheng Ong, Ji Shen Lim, Brandon Siao Xiang Ling, Francesco Leofante
Artificial Intelligence · Cross-listed from cs.DC
WavePP: High-Throughput Pipeline Parallel LLM Prefill under Prefix Reuse
Aaryam Sharma
Pipeline parallelism can improve prefill throughput by processing multiple request chunks concurrently across different stages of the model. However, keeping the pipeline fully utilized requires efficient scheduling and request preparation. In systems where stages retain and evict cache state independently, a local cache hit does not guarantee that the same prefix can be reused across the pipeline. Here, coordination overhead can impede request admission cadence and thus reduce overall throughput. In this paper, we present WavePP, a prefill runtime built on top of TensorRT-LLM that addresses these challenges by overlapping request admission with pipeline execution. WavePP asynchronously finds a prefix that can be reused across all stages, protects the cached state, and reserves space for the remaining input while earlier requests continue to execute. It subsequently plans the chunk sizes of each request dynamically to maximize pipeline fill. Each stage then completes the local preparation before executing the request. In the same system and pipeline
topology, WavePP improves TensorRT-LLM's prefill throughput in 37 of 40 tested settings on GLM 5.2 and MiniMax M2.7. At concurrency 128 with high cache reuse, these changes increase throughput by factors of 2.91 and 2.02, respectively. Across 28 Kimi K3 settings, WavePP also has the highest measured throughput in all 18 settings at concurrency eight or higher, compared with tensor/expert-parallel and pipeline-parallel baselines from TRT-LLM, SGLang, and vLLM.
Read the full abstract ▾
Artificial Intelligence
Imprint Reader: From Weight-Update Readout to Behavioral Intervention
Guanxu Chen, Qihao Lin and Jing Shao
As
language models take a growing role in AI development, a natural aspiration is for them to reflect on their own learning process, as humans do, and use that reflection to improve themselves. At the same time, these models have an advantage that human learners lack, since training leaves parameter-level traces that can, in principle, be inspected directly. However, current models cannot decode these traces into an explicit account of what they have learned. To this end, we introduce the Imprint Reader, a model trained with Semantic Mount-and-Read Tuning (SaRT) to describe frozen weight updates. SMaRT mounts each update onto the Reader and uses an anchor-free meta-query to elicit a natural-
language description, while no-change and random-perturbation controls discourage unsupported claims. On held-out updates, the joint Reader reaches judge-based Pass@100 of $2\%$ for knowledge and $16\%$ for behavior. These results demonstrate the feasibility of natural-
language readout while pointing to reliability across updates as the next step. Beyond free-form generation, the Reader provides a differentiable proxy for the gap between a specified target behavior and a candidate weight update. Its coordinate-aligned gradients support intervention through MetaEdit. At a $0.5\%$
pruning rate, Reader-guided selection raises measured harmful-
prompt refusal from $57.9\%$ to $64.1\%$ under a safety-maintenance target. Using behavior descriptions without target-task training data, MetaEdit increases the frequency of backtracking and sub-goal expressions in mathematical
reasoning traces and raises BFCL Overall from $41.69\%$ to $44.60\%$.
Read the full abstract ▾
Artificial Intelligence
Towards Reliable AI Data Scientists: Data Agents with Workflow Harnesses
Huachi Zhou, Yujing Zhang, Jiahe Du et al. (+7 more)
Large
language model
agents are increasingly deployed for data-intensive work, yet reliable data analysis requires more than general-purpose
reasoning and ad hoc tool augmentation. Data
Agents, equipped with workflow harnesses, offer a promising paradigm for automating the end-to-end data science lifecycle. This paper examines Data
Agents from a harness-centric perspective. First, we introduce a taxonomy of Data
Agents and associated data environments, organizing the literature around five functional stages: perception, planning, execution, verification, and repair. Second, we analyze the key technical routes within each stage, identifying 15 distinct approaches ranging from data structure probing to data state reconstruction. Third, we identify four open reliability problems: inactive semantic calibration, missing clarification, missing experience transfer, and the missing verification-repair repository. These problems explain why silent failures can persist even when individual components function correctly, highlighting the need for rigorous workflow harnesses and shared reliability resources. Finally, we summarize the horizontal task families of Data
Agents, examine their vertical application settings, and
benchmarks for evaluation, while maintaining a companion repository at https://github.com/DEEP-PolyU/Awesome-Data-
Agents.
Read the full abstract ▾
All authors Huachi Zhou, Yujing Zhang, Jiahe Du, Jiacheng Cai, Zijin Hong, Chuang Zhou, Zheng Yuan, Qinggang Zhang, Qing Li, Xiao Huang
Artificial Intelligence · Cross-listed from cs.RO
Spatial Grafting: Grounding 3D Features for Flow-Matching Robot Policies
Dingsheng Liu, Yangzheng Wu, Mahboubeh Asadi et al. (+5 more)
Pretrained
robot manipulation policies such as
vision-
language-action models (VLAs) or world-action models (WAMs) leave interaction-relevant metric geometry implicit. Recent breakthroughs in spatial reconstruction can supply the necessary geometry reliably, but their features describe local shape without stating where it lies with respect to the
robot. How best to deliver these features to a pretrained policy remains unresolved. We propose Spatial Grafting, a versatile, lightweight spatial module that binds frozen reconstruction features to metric,
robot-relative geometry. Spatial Grafting constructs metric-grounded spatial
tokens and injects them into the flow-matching action expert through cross-
attention, without modifying the host's perceptual pathway, so the host retains the full benefit of its pretraining. We evaluate it more broadly than any geometry-aware policy we compare against: one graft architecture, with no per-host redesign, on two VLAs and two WAMs, across four simulation
benchmarks that span short-horizon manipulation, visual robustness, clutter and long-horizon mobile manipulation, and on three real-
robot platforms with single- and dual-arm configurations. On RoboTwin 2.0, a dual-arm manipulation
benchmark, the graft improves every host across VLAs and WAMs. Grafted $\pi_{0.5}$ gains 11.3% and 15.6% on clean and randomized scenes, reaching 94.0% and 92.4%, above the strongest published 3D-conditioned policy, WAM4D (93.8% and 89.9%). The margin widens as the horizon lengthens: on tasks from BEHAVIOR-1K, a dual-arm mobile manipulation challenge scored by average task progress, it surpasses the 2025 challenge winner on five of six tasks,by up to 0.47 Q-score, and exceeds a map-conditioned spatial policy on average across the three tasks both report.
Read the full abstract ▾
All authors Dingsheng Liu, Yangzheng Wu, Mahboubeh Asadi, Zhiyuan Li, Jinbang Huang, Yixin Xiao, Tongtong Cao, Yingxue Zhang
Artificial Intelligence
EP-Mem: Elastic Privacy Memory for Social Relationship-Aware LLM Agents
Fengzhou Sun, Yuan Zhang, Xintong Yu et al. (+1 more)
Large
language model (LLM)
agents face critical
privacy risks when acting as delegates in human-
agent-human communication. To prevent such breaches,
agents must understand users' social relationships and adhere to context-dependent social information disclosure boundaries. Current studies on
agent memory privacy focus on instantaneous interactions, leaving the long-term relational disclosure problem unexplored. In this paper, we propose EP-Mem, an Elastic
Privacy Memory architecture that reframes
privacy as user-owned boundary control across social roles. EP-Mem introduces (1)
token-level
memory driven by user-configurable a
privacy policy that stratifies persons and events, combining domain-level default circulation rules with fact-level whitelist/blacklist exceptions; and (2) a pluggable sidecar with a
privacy engine that aligns disclosure controls with
memory across summary, detail, and boundary granularities, enforced throughout generation, storage, and
retrieval. We construct EP-Bench, to our knowledge the first long-term multi-party
benchmark with cross-session correlated events for policy-conditioned relational disclosure. Experiments show that EP-Mem achieves 94.0%
privacy classification accuracy, improves disclosure-permission judgment from 22% to 68%, and reduces
privacy leakage by 75.6%, while maintaining
retrieval performance and cross-
benchmark generalization.
Read the full abstract ▾
All authors Fengzhou Sun, Yuan Zhang, Xintong Yu, Jinyao Yan
Artificial Intelligence · Cross-listed from cs.CV
Hao-Xuan Ma, Yihao Liu, Yutao Sun et al. (+8 more)
Latent test-time scaling improves
reasoning by refining hidden states during inference, but existing methods typically apply a single scalar reward to all editable latent
tokens. For
multimodal large
language models, this global update ignores that generated
tokens play different roles: some are sensitive to visual evidence, while others correspond to uncertain
reasoning decisions. We present
Token-Disentangled Latent Test-Time Scaling, an inference-time framework that makes latent refinement
token-role-aware. Starting from an initial generated trajectory, we optimize a short hidden-state prefix while routing perception-side visual feedback to image-sensitive
tokens and
reasoning feedback to high-
entropy tokens.
Tokens selected by neither route are constrained by an anchor regularizer. Across both perception and
reasoning benchmarks on Qwen2.5-VL-7B and InternVL3.5-8B, our method lifts macro accuracy over CoT by +2.57 and +1.51 respectively, and outperforms strong output-space test-time scaling baselines under matched decoded-candidate budgets. Code is available at https://github.com/Qwen-Applications/TD-LTTS.
Read the full abstract ▾
All authors Hao-Xuan Ma, Yihao Liu, Yutao Sun, Yanting Miao, Mengyu Zhou, YiCheng Xiao, Long Chen, Zhenguo Li, Han-Jia Ye, Xiaoxi Jiang, Guanjun Jiang