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All fields2585Multimodal450Agents435Frontier LLMs389Inference & serving224Image segmentation154Graph learning1413D scene130Learning verifiable rewards118Voice AI71Classifier & causal discovery69Forecasting time66Flow matching57Teacher policy distillation47Adversarial training43Machine learned interatomic39All 23 fields

Open problems across fields

  • Reliable request execution in streaming video generation1 paper raises it · 0 address it

    Models often fail to follow incremental user requests while maintaining state consistency in real-time video generation.

    • StreamEval: Evaluating Interactive Streaming Video Generation Modelsraises
  • Brain-body mismatch in recursive self-improving agents1 paper raises it · 1 addresses it

    Evolving execution scaffolds (harnesses) can outpace or restrict the capabilities of the underlying model, leading to misalignment.

    • CoEvo-RSI: When Harnesses Evolve, Which Models Should Follow?raises · addresses
  • End-to-end deep search in real-world web environments1 paper raises it · 1 addresses it

    Agents struggle to autonomously explore, integrate, and verify evidence from diverse, interactive, and heterogeneous web sources.

    • Deep Search in Real-World Web Environmentsraises · addresses
  • Length generalization in state tracking1 paper raises it · 1 addresses it

    Models trained on short sequences often fail to generalize to much longer sequences without explicit progress alignment.

    • PAR: Progress-Aligned Recurrence for Length Generalization in State Trackingraises · addresses
  • Evidence-grounded evaluation for audio-visual reasoning1 paper raises it · 1 addresses it

    Existing benchmarks often allow models to exploit textual or unimodal shortcuts, failing to test true multi-modal reasoning.

    • EvaOmni: An Evidence-Grounded Benchmark for Audio-Visual Reasoningraises · addresses

Named by an AI from these papers' abstracts.

Papers

  • arXiv cs.AIOct 13

    Learning from Self-Generated Responses: Reliability--Uncertainty Self-Distillation for Reasoning

    Jinhao Pan, Bowen Wei, Yuqing Zhou, Ziwei Zhu

    Reference solutions and question-specific rubrics are not always available to guide the teacher in self-distillation. A large language model's own solution attempts offer an alternative, but their quality is difficult to assess in open-ended tasks without automatic correctness checks, and even correct attempts can involve uncertain token predictions. We propose Reliability--Uncertainty Self-Distillation (\method{}) to learn from these attempts. The same model serves as teacher and student: the teacher receives the question and completed attempt, and the student matches the teacher's token-level predictions using a question-only prompt. \method{} weights each attempt's distillation loss by additively combining judged reliability, estimated from a self-judge's score and confidence, with student predictive uncertainty. Across eleven reasoning benchmarks, \method{} outperforms all five baselines in average accuracy, reaching 70.47\% on Qwen3-4B and 76.69\% on Qwen3-8B (8.90 and 2.10 percentage points above the base models). Data and code are available at https://github.com/JP-25/RUSD.

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  • arXiv cs.AIOct 13

    Verification Turns Capability Gaps into Routing Decisions: Small Language Models for Clinical Documentation

    Nina Fatehi, Meysam Ghaffari, Animesh Agarwal, Anushka Swarup, Carlos Morato

    Clinical note generation from doctor--patient dialogue is high-volume and hallucination-intolerant: a fabricated laboratory value can create patient-safety risk, not a quality regression. We ask whether an agentic \emph{verification} architecture, rather than generator scale, can make a small language model viable here. Holding a four-evaluator verification stack and refinement policy fixed and swapping only the generator between a 4B-parameter model with a QLoRA adapter and a frontier baseline, the small model reaches $0.984$ claim-level factual entailment against $0.992$ and comes within $3.3$ points on completeness, generating entirely on-device. Its deficit is confined to \emph{inter-section structural fidelity}---arrangement rather than clinical comprehension, and the axis on which it also falls furthest below the corpus's reference notes. Because that deficit is an explicit verifier decision rather than a silent quality difference, it becomes a routing policy: run the small model by default, escalate what the verifier rejects. Measured end to end, that it achieved a similiar verified-rate acceptance to the frontier-only acceptance in this test set ($99.6\%$ vs.\ $98.4\%$…

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  • arXiv cs.SEOct 13

    Beyond Functional Correctness: Static Code Quality Profiling of LLM-Generated Code

    Jue Huang, Tarek Mahmud, Meiru Che, Guowei Yang

    Large language models (LLMs) are increasingly used for code generation, yet most evaluations still focus on whether generated code passes benchmark test suites. Functional correctness alone, however, provides only a limited view of quality: benchmark tests are often incomplete, and passing them does not guarantee that code is maintainable, reliable, or well-structured. This paper {presents a large-scale static quality profiling of} LLM-generated Python code, with a focus on maintainability and reliability. Using 65{,}663 programs from 73 models in LiveCodeBench, we evaluate generated code with established static analysis tools, including Pylint, Radon, and SonarQube. Our results show systematic differences across models that are not captured by correctness alone, with maintainability emerging as the clearest source of variation. We further find that correctness often comes with structural trade-offs rather than uniformly simpler solutions. Overall, our findings motivate evaluation frameworks that assess LLM-generated code beyond pass rates alone.

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  • arXiv cs.CLOct 13

    Fantastic Metrics and How to Find Them: Meta-Evaluating Metrics for Multi-lingual Style-Personalized Generation

    Anubhav Jangra, Adrian de Wynter

    Meta-evaluation of text generation metrics and LLM-judges is primarily conducted using metric-human correlations. However, prior work has shown that correlation is not an appropriate estimate of a metric's evaluation capability and can be unstable across evaluation settings, such as different datasets or languages. Therefore, we propose a regression-based meta-evaluation of metrics that learns on calibrated metric outputs to predict human ratings. We ground this measurement relative to a trivial constant-mean baseline and human agreements to provide a comparable measure of metric capability across evaluation settings. To test our meta-evaluation framework, we developed a large-scale multi-lingual style-personalized text generation (SPTG) benchmark, collecting multiple annotations across 10 topologically diverse languages and four evaluation dimensions, each with varying degrees of subjectivity. We meta-evaluated 32 metrics from three style evaluation paradigms (string-based, embedding-based, and LLM-judges) and their ensembles over these 40 evaluation settings. Our analysis revealed that even simple string- and embedding-based measurements can match strong LLM-judges when…

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  • arXiv cs.AIOct 13

    ListQA: A Benchmark for Evaluating List-Formatted Factual Knowledge Retrieval in Large Language Models

    Mayank Kulkarni, Rakesh Vaideeswaran

    Many real-world questions demand not a single fact but an organized collection of them, yet existing factual knowledge benchmarks almost exclusively target single answer retrieval. We introduce ListQA, a benchmark of 9,045 human-curated, cross-validated questions (audited error rate of 1.7%) that require LLMs to recall multiple facts and compose them into structured lists of 3-10 elements. Questions range from flat lists ("Which NCAA teams went undefeated between 2000 and 2024?") to hierarchical lists with sub-attributes ("...and what was their record and result?"), spanning eight categories across 60+ countries. An evergreen design (explicit temporal constraints or historically immutable facts) keeps ground truth valid without periodic updates. For evaluation, we frame element matching as a linear sum assignment problem: optimal bipartite matching paired with LLM-based semantic grading jointly captures factual accuracy, hallucination, and format compliance. We benchmark 34 models (8 families, 42 configurations across standard and thinking enabled inference) and find: the best standard-inference model scores below 37% LLM-Judge-F1, multi-faceted questions are harder across the…

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  • arXiv cs.CLOct 13

    From Qualitative Text to Integrated Evidence: The Parallel-Then-Integrated (PTI) Procedure

    Elyas Irankhah, Ashley Hagaman, Samah Fodeh

    Mixed methods research offers established strategies for integrating qualitative and quantitative evidence, but less guidance for integrating two computational representations of the same qualitative corpus. This methodological study introduces Parallel-Then-Integrated (PTI), a four-step procedure for explicit and traceable integration. We present PTI using 1,203 interview paragraphs from 29 mothers in Nepal. The two representations were ontology-guided Patient Voice (PV) coding with PVminer and data-driven themes generated through non-negative matrix factorization (NMF). A theme-complete crosswalk documented convergence, divergence, and preprocessing-sensitive differences. Depression status, based on Patient Health Questionnaire-9 (PHQ-9) scores, served as a secondary external anchor for exploratory association checks. PTI provides a structured approach for integrating computational representations of qualitative text within mixed methods research.

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  • arXiv cs.AIOct 13▲ 155

    Into the Unknown: Benchmarking AI agents on open scientific research problems

    Seungone Kim, Jiayi Geng, Young-Jun Lee, Bo Liu, Juyoung Suk +23

    Can AI agents carry out research on open scientific problems? We introduce Into the Unknown, a benchmark that provides an agent with a science paper along with a research direction to pursue and tasks it to write a follow-up paper. We work with scientists at US Department of Energy (DOE) national laboratories, who each contribute an unpublished follow-up direction from their own work together with the necessary conditions a submission must meet. While assessing the quality of a follow-up paper is challenging in general, using these conditions to form a rubric makes evaluation more tractable for research problems that have no reference answer. We have nine frontier agents write 15 follow-ups each, spanning quantum computing, plasma physics, computational chemistry, materials science, and scientific machine learning domains. None of the 135 submissions satisfies all the conditions, and 114 fail our integrity check, most often because the paper claims work that was never done or reports numbers that contradict the recorded evidence. Overall, agents readily produce papers that look like follow-ups, but none meets the standard of the domain scientist, leaving substantial headroom. Our…

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  • arXiv cs.AIOct 13

    AgentCIScope: Grounded Contextual Integrity Evaluation for LLM Agents

    Chengquan Guo, Zhaorun Chen, Zinan Lin, Xiaodong Liu, Bo Li

    LLM agents are becoming increasingly capable of handling daily workspace tasks by interacting with application environments through tool calls. However, as agents read and write sensitive user data across email, documents, and other applications, a central privacy question is whether they preserve contextual integrity (CI), that is, whether the information flows they produce comply with context-specific disclosure constraints. Existing work either focuses on model-level privacy leakage or evaluates agents in limited application environments with manually constructed disclosure constraints. In this work, we introduce AgentCIScope, a unified platform and benchmark for evaluating agent-level contextual integrity through executable, multi-step tasks across diverse, realistic application environments. AgentCIScope transforms public and synthetic privacy cases into executable agent tasks, with disclosure constraints grounded in three criteria: governing privacy policies, persona-based agreement, and a predefined sensitive-attribute taxonomy. Based on AgentCIScope, we evaluate 12 agents on 936 tasks and find that strong task-completion capability does not guarantee contextual-integrity…

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  • arXiv cs.SEOct 13

    DivAlgo: A systematic evaluation of algorithmic diversity for code generation models

    Anubhav Jangra, Smaranda Muresan

    Code generation models are actively being used in professional and educational settings. However, the competence of large language models (LLMs) to generate algorithmically diverse code remains largely unknown. We propose the DivAlgo benchmark, a large-scale dataset constituting algorithmically diverse solutions to programming problems. We also introduce a lightweight similarity metric to enable large-scale evaluation. Our experiments across 24 modern code generation models found that uncontrolled model-generated code is significantly less diverse than human solutions (capturing ~31% of human diversity), and even the best diverse code generation strategy could achieve only ~70% of human diversity. Moreover, while it was possible to steer the models to generate solutions based on human strategies, they were unable to reproduce the diversity of the human solutions. These findings indicate the limitations of code generation models in capturing the algorithmic diversity humans exhibit, and that overexposure to model-generated code can narrow programmers' algorithmic vocabulary.

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  • arXiv cs.CVOct 13

    Agronomic Characterization and Time-Series UAV Phenotyping to Support Field Pea Breeding in Iowa

    Yanben Shen, Joscif Raigne, Asheesh Singh, Arti Singh

    Field pea has gained interest in the Midwest U.S. and has the potential to benefit Iowa cropping systems. This study evaluated 17 accessions and 7 Canadian varieties for agronomic and seed yield performance. This study found that PI accession trials exhibit genetic diversity in phenology and canopy traits, whereas the Canadian elite varieties demonstrated great yield potential under Iowa conditions. The accession genotypes matured between 75 DAP (late June) and 95 DAP (late July) across six site-years. The top-yielding Canadian varieties averaged 1961 kg/ha across two site-years. To comprehensively assess field pea performance, this study extracted time-series Unmanned Aerial Vehicle(UAV)-derived traits, assessed UAV-derived traits, and evaluated their correlations with multiple agronomic traits and yield. The results found that UAV-derived time-series canopy area was the most yield-relevant trait (r = 0.63) among all UAV-derived canopy features, vegetation indices, and manually collected measurements. Compared with yield, UAV-derived canopy area showed higher selection reliability (H2 = 0.87, CV = 13.46%), making it suitable for phenotypic selection and accelerating Midwest…

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  • arXiv cs.SEOct 13

    A Systematic Study of Deprecated Android API Usage Updates

    Tarek Mahmud, Meiru Che, Guowei Yang

    Android Application Programming Interfaces (APIs) continuously evolve, causing existing APIs to become deprecated and requiring developers to migrate their usages to recommended replacements. Although prior studies have proposed automated migration techniques, their effectiveness is often limited by the diversity and contextual complexity of API changes. This paper presents a systematic study of deprecated Android APIs, their recommended replacements, and real-world usage updates performed by developers. Based on an analysis of 1,000 deprecated APIs and 4,336 migration examples collected from GitHub, we identify six migration categories and derive recurring usage-update patterns involving Android-version checks, parameter adaptation, object initialization, data-type conversion, and changes in API interaction mechanisms. To assess their applicability, we augment the zero-shot GUPPY benchmark prompts with the migration category and corresponding usage-update pattern. On 156 benchmark tasks, the pattern-guided prompt improves correct updates from 139 to 147 and performs better in five of the six categories.

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  • arXiv cs.AIOct 13

    Compiling Expert Procedures into Verified Programs: When a Small Model Is Enough

    Vishvesh Bhat, Asad Aali, Muhammad Ahmed Mohsin

    In procedural tool-use domains, how much of the gap between base models is procedure rather than capability? We build an explicit program from a teacher's traces and run it in front of unchanged small models, including an 8B model. The construction, PLVR , takes the reasoning traces of a teacher, Kimi-K3, clusters their segments to locate recurring operations, has a frontier model author one typed primitive per operation, each run by a small frozen model and assembles the program by A* search over a mined rule library, under a reference-action objective. At inference the program takes one of two forms: for troubleshooting and tool search it produces the chain of thought the student would otherwise generate for itself, and the student acts on it; for multi-turn tool matching the student proposes each action and the program checks, repairs, or retries it before it goes out. On the official tau2-Bench Retail and Telecom test sets and on LiveMCPBench, with three Nemotron base models and the same program for each, the answer depends on the domain. On Telecom, Granite-4.2-8B as the student goes from 43.0% to 91.2% under the program, and every Nemotron student reaches 93.3-94.4%,…

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  • arXiv cs.LGOct 13

    Demystifying On-Policy Distillation: Where Does the Teacher's Advantage Live?

    Yiping Li, Fan Zhao, Zhibo Hou, Zhiyu An, Wan Du

    On-policy distillation (OPD) is increasingly studied as a cheaper alternative to reinforcement learning, yet it remains a form of knowledge distillation (KD) that relies on teacher supervision. We revisit OPD from the KD perspective by asking where the teacher's advantage lies along student-generated trajectories. Controlled token flips at teacher-student disagreements directly measure the accuracy gained from teacher corrections. Single-token flips yield a modest average gain on selected failed trajectories, whereas repeated flips raise accuracy from 28.4% to 74.5%, close to the teacher's 76.9%. Gains continue to accumulate with more flips, but mass- and KL-based selection outperform random flips by 15-18 percentage points at counts of similar magnitude. Selective intervention remains useful after OPD, but greater argmax agreement does not consistently improve accuracy across model pairs. We advocate training schedules that learn more valuable teacher corrections while maintaining distributional stability. Controlled flips and fork-resolution analysis evaluate OPD beyond accuracy by measuring which teacher preferences the student acquires and what further correction can recover.

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  • arXiv cs.LGOct 13

    Towards Training and Inference of Graph Neural Networks in Graph Databases

    Victor Pekkari, Harry Wik, Alfred Clemedtson, Borun Shi, Xuan-Son Vu

    Graph Neural Networks on large graphs commonly rely on in-memory graph storage with ad hoc data processing pipelines for both training and inference. Graph databases provide scalable storage and efficient traversal for graphs that can evolve over time, but their applicability in GNN training and inference are rarely studied. We show that graph DBs can be used in GNN training and inference directly, executing sampling, feature retrieval, and even the full forward pass. We show different GNN training pipelines can perform on-the-fly sampling and retrieval in graph DBs using Cypher. We further provide optimisations that speed up the training process by \~2.5x while maintaining the same accuracy. We illustrate that fully in-DB inference is possible, independent of how the GNNs are trained. Our inference setup can be >10x faster than cold loading the graph into RAM with no extra memory overhead on real-world graphs. Our work lays the foundation for in-graph-DB GNN training and inference.

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  • arXiv cs.DSOct 13

    Online Tokenization with Optimal Delay in Linear Time

    Mina Dalirrooyfard, Christopher Ye

    Tokenization is an essential preprocessing step in almost all large language models (LLMs). One of the most popular frameworks is BPE tokenization, which consists of special token segmentation, pretokenization, and byte-pair encoding (BPE). However, existing algorithms are sequential, with each stage only processing complete output from the previous stage. In order to reduce latency and memory footprints, recent works (Jiang and Gong, ICML 2026) (Li, Yang and Mamouras, ASPLOS 2026) (Mamouras, Li and Yang, PLDI 2026) have initiated the study of online algorithms for subroutines such as pretokenization and BPE with a goal of streaming input to output as the input arrives online. However, the search for efficient online tokenization remains open. In this work, we formalize and resolve online tokenization. We introduce the notion of delay for online tokenization which characterizes the extent to which the algorithm output lags behind the (possibly computationally intractable) optimum. We give a near-linear time algorithm with optimal delay (up to constant factors). We complement this algorithm with a conditional lower bound: unless $\mathbf{P} = \mathbf{NP}$, there is no polynomial…

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  • arXiv cs.LGOct 13▲ 7

    MA-Bench: Measuring Operational Planning in Language Models

    Rodney Lafuente-Mercado, Robert Sincero, Phil Sage, Yuan Xue

    How well can a language model turn a large, incomplete and partly contradictory body of documents into a plan when no single correct answer exists? We introduce Mission Analysis (MA)-Bench, built on mission analysis, the first step of military operational planning, in which a staff reads the full situation and states the problem before anyone proposes a solution. Its 10 expert-built scenarios pair synthetic packets of ten to twelve documents with 1,257 expert-written pass/fail requirements, each containing information on their source, question type, and importance level. A reasoning-model judge grades each brief against the requirements without seeing the documents; it grades like a fifth credentialed planner, leaving a four-planner panel's agreement unchanged (Fleiss $κ$ 0.42 to 0.41), and a judge from a second provider reproduces the model ranking (Spearman 0.94). Across twenty frontier and open-weights models the best gets 62% of the rubric items right. Models are strongest on facts stated in the documents (58%) and weakest on judgments that must be inferred (33%), and six failures in ten are omissions rather than errors. MA-Bench grades the determinations, not the prose. We…

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  • arXiv cs.AIOct 13

    Graph-Based Orchestration of Heterogeneous AI Models: A Control Node Approach to Composite Intelligence

    G. Michael Youngblood, Filip Dvořák, Slavo Švancár, Tomáš Balyo, Michal Ficek +1

    We present a graph-based framework for Composite AI systems that integrate multiple AI techniques to address complex problems. We describe an architecture using specialized control nodes to orchestrate diverse models including planning, machine learning, constraint programming, and large language models. The system employs directed acyclic graphs and behavior trees to manage model relationships and execution sequencing. Our methodology encompasses data connectors, computational services, and a containerized deployment framework supporting hierarchical, sequential, and concurrent model composition patterns. Through case studies, we demonstrate how this architecture enables the creation of composite systems that leverage the strengths of multiple AI techniques within a unified operational schema, overcoming the limitations of single-model approaches.

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  • arXiv stat.MLOct 13

    Student Copula Markov Networks

    Salha Mamane, Cheikh Tidiane Seck

    This paper extends the applicability of probabilistic graphical models to non Gaussian data. It establishes a characterization of the pairwise Markov property in undirected probabilistic graphical models in terms of copula densities. Building on this characterization, we derive a necessary condition for the pairwise Markov property in Student copula Markov networks. An Expectation-Conditional Maximization algorithm (ECM) is provided to facilitate the penalized maximum likelihood estimation and graph selection. The performance of the proposed algorithm is evaluated through simulation studies, which demonstrate its effectiveness in estimating model parameters and recovering the underlying graph structure.

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  • arXiv cs.CLOct 13

    CAFE: Confining Feature Distillation to Language-Routed Adapters for Compact Multilingual Automatic Speech Recognition

    Tianchi Liu, Nicholas Moratelli, Khurram Azeem Hashmi, Mohammadreza Zolfaghari, Rishabh Jain +3

    Feature distillation is usually expected to add to logit distillation. We find that this expectation does not hold for two common feature losses in compact multilingual automatic speech recognition (ASR), and analyze why through their gradients. The gradient of a projection-based loss is orthogonal to the task gradient, while that of Centered Kernel Alignment (CKA), a similarity-based loss, is weakly aligned with it. CKA lowers the word error rate (WER) on non-English languages, but at the expense of data-rich English. Three loss-level remedies (one trained, two probed) all fall short, and the CKA gradient overlaps with the subspace English uses in the shared encoder by 97.5% to 99.5% (about 81% by chance). Guided by this diagnosis, we propose CAFE (Confined-Adapter Feature distillation), which confines the CKA gradient to adapters that English bypasses, addressing the trade-off in the parameters rather than in the loss. On the Open ASR Leaderboard, CAFE lowers the average WER from 8.55% to 8.02%, beats an adapter-only capacity control at 8.51%, and makes English more stable across checkpoints. At 254M parameters, it trails the 0.6B teacher by 2 WER points and is competitive with…

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  • arXiv cs.LGOct 13

    TRACER: Trace-Regularized Training Against Indirect Prompt Injection

    Kshitij Mishra, Salem Lahlou, Nils Lukas

    Indirect prompt injection uses untrusted retrieved content, tool outputs, or agent messages to override an LLM's intended behavior. We show that defense robustness is channel-conditioned: the same attack succeeds 18% of the time as a follow-up message but 53% when placed in retrieved context. We propose TRACER (Trace-Regularized Adversarial Channel Exposure with Reward-shaping), a channel-aware adversarial training method that samples presentation wrappers and combines an answer-anchored, evidence-gated reward with ATRC, which aligns attacked reasoning with clean evidence when an injection is invalid. On GSM8K with Qwen2.5-3B-Instruct, TRACER reduces RAG-framed ASR from 65.5% to 6.1% while preserving 78-82% clean accuracy. Joint RAG+tool training achieves 13.9% average ASR across framings at 3B scale, while a single epoch at 7B reaches 9.4%. These results demonstrate that robust defenses must explicitly train and evaluate across deployment channels.

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Rising research topics

  • Federated learning39−13.3%
  • CAD models13−13.3%
  • Adversarial training43−14.0%
  • Anomaly detectors30−16.7%
  • Forecasting time66−17.5%
  • Inference & serving224−23.3%
  • Frontier LLMs389−25.2%
  • Agents435−26.4%