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All fields3184Multimodal511Agents492Frontier LLMs422Inference & serving255Image segmentation1703D scene153Learning verifiable rewards149Graph learning143Voice AI95Classifier & causal discovery85Forecasting time76Flow matching68Evals66Teacher policy distillation48Federated learning48All 32 fields

Open problems across fields

  • General in-context learning for embodied agents1 paper raises it · 0 address it

    Enables models to adapt to new tasks and environments without retraining, improving robustness in real-world deployment.

    • Has GPT-6-Astra Achieved Embodied Intelligence?raises
  • Cost and latency in embodied control loops1 paper raises it · 0 address it

    High computational and time costs limit real-time applicability of agentic systems in dynamic environments.

    • Has GPT-6-Astra Achieved Embodied Intelligence?raises
  • Physical-state verification and failure recovery1 paper raises it · 0 address it

    Agents must reliably detect and correct errors in physical interactions to avoid irreversible failures in real-world tasks.

    • Has GPT-6-Astra Achieved Embodied Intelligence?raises
  • Long-horizon task preservation in embodied agents1 paper raises it · 0 address it

    Maintaining progress over extended sequences is critical for complex, multi-step tasks in robotics and navigation.

    • Has GPT-6-Astra Achieved Embodied Intelligence?raises
  • Open-source stacks for generalist computer-use agents1 paper raises it · 1 addresses it

    Closed systems hinder reproducibility and systematic study of agent design for real-world computer tasks.

    • OpenGCUA: Toward a Fully Open Stack for Generalist Computer Useraises · addresses

Named by an AI from these papers' abstracts.

Papers

  • arXiv cs.LGOct 16

    Latent State Utilization in Probabilistic Circuits

    Zilei Shao, Anji Liu

    Probabilistic circuits (PCs) are generative models that support efficient probabilistic inference. Adding latent states to a PC increases its representational capacity, but yields diminishing gains in likelihood. We use mutual information to examine how the dependence represented by a trained PC changes with latent size. Experiments with Hidden Chow-Liu Trees (HCLTs) on ImageNet32 show that learned dependence grows slowly and remains well below both the latent-state upper bound and estimates of dependence in the data. To understand how learning uses these states, we examine how strongly each sample favors particular latent states and how broadly states are used across the dataset. Tracking these measurements during HCLT training distinguishes rapid early posterior concentration from the more gradual stabilization of sample-dependent state preferences. Early measurements also guide initialization selection in HCLTs and hidden Markov models (HMMs), as well as tree selection in HCLTs. The resulting choices improve the ultimate performance across the tested latent sizes, providing a practical benefit from our study of latent-state utilization.

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  • arXiv cs.HCOct 16

    Indirect Influences of Artificial Intelligence Advice on Complex Sepsis Treatment Decisions

    Venkatesh Sivaraman, Joel Levin, Eric P. Mason, Victor Talisa, Suchi Saria +4

    Sepsis is a major public health concern, and many sepsis patients die due to ineffective hemodynamic management. Artificial intelligence could support hemodynamic decisions, particularly in complex cases where conflicting information and comorbidities drive uncertainty and practice variation. However, these systems' real-world value depends on not just accuracy, but how they influence the quality of clinician decisions. We first retrospectively evaluated an AI system's recommendations in 6,928 sepsis patients, finding that they improved upon the original providers' decisions overall but had significantly worse performance in complex cases. We then conducted a pre-registered experiment with 228 US critical care physicians to test their use of this imperfect advice in six complex treatment decisions, using one of three advice types designed to vary reasoning autonomy. Participants given simple model recommendations overcame poor advice, choosing optimal treatments in 56.3% of decisions compared to 31.8% if they had always followed the recommendations. They also made significantly better decisions than those receiving a less prescriptive advice type (42.9%). Physicians selectively…

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  • arXiv cs.CROct 16

    RATION: Towards Quantitative Risk-Based Access Control for AI Agents

    Yuntao Du, Harsh Chaudhari, Yuetian Chen, Ron Heichman, Ashish Jayamohan +6

    AI agents increasingly interact with external environments to complete complex user tasks. These interactions can lead to unexpected behavior and expose agents to security threats such as indirect prompt injection. Current agent security systems largely protect agents by restricting their actions, which can result in a substantial security-utility trade-off for complex, open-ended tasks. To address this, we propose a quantitative risk-based approach to access control for AI agents that regulates measurable risks accumulated during task execution. As a concrete realization of this approach, we introduce RATION, a security framework that tracks measurable risks associated with agent actions and enforces quantitative constraints throughout execution. RATION first constructs a task-specific policy by combining explicit constraints derived from the user request with implicit risks calibrated from historical executions of similar tasks. During task execution, RATION continuously tracks the accumulated risks and applies different enforcement strategies depending on the type of risk. Extensive experiments on four security benchmarks show that RATION provides strong security while…

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  • arXiv cs.LGOct 16

    Learning Administrative Boundary-Aware Location Encoders via Reverse Geocoding

    Lakshay Sharma, Alex Marin

    Location encoders learn compact representations of latitude--longitude coordinates for downstream geospatial prediction tasks. Existing approaches are pretrained using continuous or naturally occurring supervision, such as imagery, climate variables, or species distributions, and therefore do not explicitly capture administrative boundaries or place names, despite their strong influence on many real-world geospatial phenomena. We introduce, to our knowledge, the first method for learning administrative boundary-aware location encoders, using reverse-geocoded text as a self-supervised training signal. Our approach can be applied to existing location encoder architectures and consistently improves their downstream performance. We further introduce RevGeoRQ, a custom architecture tailored to reverse-geocoding supervision, which yields additional gains and exceeds contemporary location encoders on several downstream tasks. Finally, the use of human-interpretable reverse-geocoded strings and residual-quantized semantic IDs provides a more interpretable representation of geographic location than existing approaches. Code and model weights to be released soon.

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  • arXiv cs.LGOct 16

    Muon Operates Beyond the Edge of Stability

    Niko Covic, Baraq Lipshitz, Alexander Padula, Stefan Visnjic

    We study the Muon optimizer using sharpness and provide the first empirical evidence that the Flat Minima Hypothesis also applies to Muon. Further, we derive Muon's implicit preconditioner to analyze its potential Edge of Stability behavior. Experiments on CIFAR-10 show sharpness strongly correlates with generalization gap within each optimizer (Pearson $r = 0.45$-$0.69$, $p \leq 10^{-4}$), confirming the hypothesis holds for Muon similarly to Adam and SGD. However, Muon operates above the classical sharpness-based Adaptive Edge of Stability threshold that bounds diagonally-preconditioned optimizers like Adam and RMSprop, yet remains stable. Our findings suggest Muon operates in a fundamentally different regime than diagonally-adaptive methods. Defining the exact stability boundary for Muon remains an open question. All code is available at https://github.com/NikoCovic/DL-project.

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  • arXiv cs.AIOct 16

    CME-SpectrumBench: Can LLMs Analyze Condensed Matter Spectral Data?

    Jin Gene Wong, Anjney Midha, Joseph Mathew Tennyson, Wei-Lin Chiang, Ion Stoica +1

    Large Language Models (LLMs) are presently well assessed by expert-level mathematics and physics benchmarks which focus on theoretical, symbolic reasoning. However, we lack experimental benchmarks that evaluate data analysis in frontier research problems, a capability that future domain-specific foundation models must have. To this end, we present CME-SpectrumBench, an expert-designed benchmark consisting of 190 questions that measures how reliably frontier LLMs analyze research-level spectral scientific data in condensed matter experiments (CME). Using simulated CME spectral data, we prompt models to identify structures from 2D intensity arrays in the face of noise and limited instrumental resolution, a test of dense context reasoning. Models are scored by how accurately their numerical answers approach the ground truth. Under direct query, scores generally pale in comparison to handwritten code due to clear failure modes: models can find simple features but struggle to trace patterns or compute derived quantities, and performance typically degrades with increased data resolution and noise/convolution levels. LLM performance becomes comparable to handwritten code by allowing…

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  • arXiv cs.LGOct 16

    Torch-DPhys: A Torch-Native, MOOSE-Compatible Framework for Differentiable Physics Simulation and Hybrid Machine Learning Integration

    Akzhol Almukhametov, Yang Liu

    We present Torch-DPhys, a Torch-native framework implemented in PyTorch for differentiable physics simulation and hybrid physics-machine learning. It implements selected thermal-hydraulic modeling concepts and discretizations from MOOSE's Thermal Hydraulics Module and the System Analysis Module (SAM), with a preliminary extension to continuum mechanics. Native tensor residuals support CPU and GPU execution, compilation of selected numerical kernels, and sparse direct linear solves. Sensitivities are obtained by differentiating executed numerical operations or through discrete adjoints of converged residual equations. Verification combines analytical solutions, reference-code comparisons, and derivative checks. Demonstrations cover plane-strain elasticity, one- and two-loop thermal-hydraulic transients, and an axisymmetric molten-salt fast reactor. For the reactor, comparison with SAM-Lite gives a maximum normalized field discrepancy of 1.13 x 10^-5 over 200 adaptive time steps, demonstrating agreement between the tested discrete implementations. Selected transient sensitivities are assessed using tangent-linear calculations and finite differences on fixed time schedules. Two…

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  • arXiv cs.CVOct 16

    CRISP: Progressive Illumination Curriculum for Recurrent Low-Light Video Enhancement with Semantic Priors

    Ruirui Lin, Xin Yan Lim, Yini Li, Zhiming Liu, Nantheera Anantrasirichai

    Low-light video enhancement requires the recovery of brightness, color, and fine spatial structures while preserving temporal coherence in the presence of noise and motion. Existing methods often struggle to balance these objectives, particularly in severely degraded regions. This paper presents CRISP, a recurrent framework trained using a progressive illumination curriculum. Its two-frame recurrent architecture propagates information across successive frames to exploit temporal context beyond the immediate input pair. During training, supervision progressively transitions from intermediate targets interpolated between the low-light input and the reference to the full reference, gradually increasing the required brightness and color correction. This curriculum stabilizes recurrent training without introducing additional inference cost and yields the largest performance improvement among the components evaluated in the ablation study. To further support structural reconstruction, the framework optionally incorporates frozen DINOv3 semantic features through adaptive layer normalization (AdaLN)-style modulation, without fine-tuning the foundation model. Experiments on DID and…

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  • arXiv cs.AIOct 16

    Who Speaks, Who Authorizes, Who Acts? Understanding Trust Boundaries in Multi-Agent LLMs

    Junru Zhu, Xin Chen, Hang Lyu, Jiayue Meng, Muzi Li +4

    Multi-agent LLM systems exchange messages before acting through tools, but message delivery, signing authority, and tool-side enforcement are separate trust boundaries. We represent them as communication, authorization, and execution structures, and use the authority cut to characterize the smallest independently compromised signer coalition that can construct a policy-valid false-claim proof under stated assumptions. Controlled paired hidden-state studies reveal four distinctions. First, no fixed-message routing contrast across four models meets the prespecified large-effect criterion; when relays rewrite messages, executed false-state action rates vary by model and route configuration. Second, presented source-identity cues do not uniformly increase protected-action selection across three models. Third, on invalid-proof cases, Nemotron and Llama request protected actions in 52.1% and 54.2% of trials, respectively, yet a deterministic tool gate commits none: request-level and commitment-level safety readouts diverge. Finally, a separate single-model authorization check shows that a false claim with a policy-valid compromised-signer proof can pass the gate, since proof validity…

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  • arXiv cs.LGOct 16

    Predicting CVSS and CWE from CVE Descriptions with Fine-Tuned Small Language Models

    Tara Dixit, Jessie Dong, Summer Li

    Until April 2026, the National Vulnerability Database labeled nearly every published Common Vulnerabilities and Exposures (CVE) entry with a Common Vulnerability Scoring System (CVSS) v3.1 vector, a severity tier, and a Common Weakness Enumeration (CWE) weakness type. Now, NIST enriches only the high priority CVEs, leaving many newly published vulnerabilities unlabelled with the classifiers that security teams use for patch triage. In CVE enrichment, we predict the eight base components of CVSS v3.1 vector, base score, severity tier, and CWE from the vulnerability's textual description alone. To prevent leakage between near-duplicate descriptions, all systems are trained only on CVEs published in 2022 and 2023. Results are evaluated on all 36,325 usable 2024 CVEs in the pinned NVD snapshot. All scores are either directly predicted, or calculated from predicted vectors using the CVSS formula. A fine-tuned 3.8B-parameter model Phi-3.5-mini, trained with LoRA in 2.5 hours on a single Apple M5 Pro chip, achieves 69.9% accuracy on severity and 70.9% on CWE. It matches the official vector exactly for 48.8% of test CVEs, outperforming TF-IDF logistic regression by 2.8 to 4.7 points on…

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  • arXiv cs.AIOct 16

    Not Every User Says It All: Training Tool-Using Agents Beyond Ideal Simulated Users

    Han Bao, Zheyuan Zhang, Kaiwen Shi, Yanfang Ye

    Tool-using agents are trained and evaluated almost exclusively with strong simulated users who state every detail clearly, yet real users are often weaker. We construct weak users by masking sentences of a simulated user's replies, holding the task and the user's knowledge fixed, and cross-evaluate agents trained with strong and weak users on tau2-bench and ToolWOZ. Weak users lower the success of every policy, and training with them does not help: no policy trained with weak users, through fixed masks, mixtures or a curriculum, serves weak users better than the strong-trained policy beyond run-to-run noise, and prompting the agent to ask more does not help either. We propose StaGPO (State-Grouped Policy Optimization): the agent writes what the task needs, what is missing and how it is being filled, and GRPO uses this state to compare turns of different rollouts that reach the same situation, assigning step-level credit. Within the standard recipe of a small SFT cold start followed by RL, StaGPO makes weak-user training useful: on ToolWOZ, the Qwen3.5-2B policy trained with weak users reaches 76.8% with weak users, the highest of any Qwen3.5-2B policy we trained, 4.8 points above…

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  • arXiv cs.LGOct 16

    Constructive Universal Approximation by Infinite-Dimensional Neural Networks via Gaussian-Weighted Ridgelet Transform

    Sho Sonoda, Luca Galimberti, Yuanchao Xu, Isao Ishikawa, Masahiro Ikeda

    Neural networks whose inputs are functions, as in operator learning, are usually shown to be universal by existence arguments, either by reduction to finite-dimensional networks or by Stone--Weierstrass-type density; these prove that approximants exist but say little about how the parameters depend on the target. We take a constructive route. We extend the ridgelet transform, an integral transform related to the Fourier, Radon, and wavelet transforms, to continuous maps on a separable infinite-dimensional Hilbert space, replacing the Lebesgue measure that is unavailable in infinite dimensions by Gaussian weights with a homogeneous mixture on the direction space. The resulting transform has a Plancherel identity and reconstruction formulas incorporating the frame operator, the correction induced by the weights, and it assigns to each target an explicit coefficient measure, one for every admissible analysis filter. From these representations we obtain uniform approximation on compact sets for all continuous non-polynomial activations of polynomial growth, including ReLU, and, by Monte Carlo sampling of the coefficient, finite-width networks with a dimension-free $O(N^{-1/2})$ error…

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  • arXiv cs.LGOct 16

    Inducing Fourier Geometry: A Smoothness Prior Imposes Approximate Translation Equivariance in Multimodal Stellar Embeddings

    Ilay Kamai, Alex M. Bronstein, Hagai B. Perets

    Learned representations often contain geometric structure induced by symmetries in the underlying data. We ask whether a known continuous parameter space can instead be used to \emph{induce} such structure when the corresponding symmetry is only approximate. We introduce a graph-Laplacian smoothness prior that encourages representations to vary smoothly over a physical parameter space without specifying a representation geometry; in the continuum limit it selects Laplacian eigenfunctions and therefore predicts the Fourier-like geometry associated with translation symmetry. On multimodal stellar representations spanning six astronomical surveys, the prior induces Toeplitz Gram structure and sinusoidal modes along stellar parameters, including in modalities whose native representations lack this geometry, while modality-specific residuals preserve structure outside the imposed coordinates. Of the exact-symmetry predictions, stationarity in a recoverable warped coordinate survives; diagnostics assuming exact quantization or asymptotic rates do not. The prior also improves downstream linear-probe performance and label efficiency, suggesting a general strategy: where a meaningful…

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  • arXiv cs.LGOct 16

    An important Bottleneck in LLM Optimizers: Principle-Level Local Optima

    Nian Ran, Tianhe Jia, Zhengyang Mu, Yue Wang, Sebastian Brulin +4

    Feedback-driven LLM optimizers are increasingly used for evolutionary search, Bayesian optimization, heuristic design, and scientific discovery. Yet rapid initial gains often give way to prolonged plateaus despite substantial remaining evaluation budgets. We investigate \emph{principle-level search collapse}, where an optimizer's search becomes confined to a narrow set of design principles, even though it may continue to generate distinct candidates and achieve small improvements. We assemble a diagnostic benchmark spanning molecular optimization, network sentinel-node selection, and GPU-kernel optimization, and introduce a decision-tree protocol combining principle narrowing, prolonged performance plateaus, and gaps to attainable reference solutions. Across 161 assessable runs on the three problem domains, 78.3\% receive support for collapse, including 55.3\% with strong support. Candidate-level variation can nevertheless conceal restricted search at the principle level. We then test \emph{diversity injection}, a common response to search stagnation, such as higher t, multiple LLMs, and web search, and show that it provides limited and inconsistent benefits: among 65 molecular…

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  • arXiv cs.CLOct 16

    Beyond Clean Accuracy: The Practical Challenges and Benefits of Pixel-Based Language Models

    Haowen Tang, Wei Xia, Raoyuan Zhao, Hinrich Schütze, Yihong Liu

    Pixel-based language models process rendered text without relying on a fixed subword vocabulary, offering a promising approach to noisy and multilingual inputs. However, their practical value depends on whether they can be reliably adapted to downstream tasks. To this end, we systematically examine this question through fine-tuning behavior, typographical robustness, and zero-shot crosslingual transfer. Under our tested settings, pixel-based models require longer training and broader labelled-data coverage than text-based models, while delayed learning and learning-rate-dependent failures make adaptation less reliable. Our analyses further suggest that task-relevant information is less readily accessible in pretrained pixel representations, placing greater demands on fine-tuning. Once successfully adapted, however, pixel-based models lose substantially less accuracy under typographical noise and achieve higher target-language accuracy than English-pretrained text-based counterparts on two crosslingual benchmarks, despite weaker clean English performance. Together, these findings reveal a central trade-off: replacing tokenization with rendering reduces sensitivity to corrupted and…

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  • arXiv cs.CROct 16

    Unbounded Losses, Unchecked Inputs: Measuring the Safeguards of LLM Options-Trading Agents

    Mengxiao Wang, Nitesh Saxena

    Large language model (LLM) agents now place options orders at retail brokers, and developers publish open-source options agents faster than anyone has examined them. Prior security work on trading agents scored academic equities designs by whether an attacker can flip a buy-or-sell decision. We present the first measurement of the options agents that brokers and developers ship, scored by the safeguards they enforce and by the worst loss their documented order paths can reach. We screened 765 GitHub repositories, broker documentation, and the research literature and found 215 eligible repositories and 8 broker-native agent interfaces. We then scored 26 systems, including every broker interface and every open-source project with at least 20 stars, on seven robustness and eleven security safeguards with cell-level evidence. The ecosystem is new: 198 of the 215 repositories were created in 2026, and 129 (60.0%) appeared in the two weeks around one options-trading hackathon. Of the 22 systems that can place orders, 10 enforce a pre-trade limit outside the model, yet none of the 26 authenticate their text sources or cross-check market data. Of the 26, 17 can execute with no mandatory…

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  • arXiv cs.CLOct 16

    BanglaShield: A Script-Robust Safety Classifier and Benchmark for Bangla Native, Romanized, and Code-Mixed Prompts

    Mohsinul Kabir, Md. Tariquzzaman, Mohammad Rashidujjaman Rifat, Tharindu Ranasinghe, Nishat Raihan

    Safety classifiers for large language models (LLMs) are typically built and evaluated on standard-script text, yet Bangla, like many lower-resource languages, is routinely written in native script, in Latin-script romanization, and code-mixed with English, Hindi, or Urdu. We introduce BanglaShield, a resource and model for graded prompt-safety classification across these written forms. We consolidate 10,245 Bangla prompts from six sources under a four-level taxonomy (Safe, Offensive, Harmful, Severe) and render each prompt in three aligned forms that share labels and a group-aware split, enabling strictly paired comparisons. Benchmarking six contemporary LLMs, including a Bangla-specialized one, on 36,882 classification queries and 24,318 judged responses, we find that LLM classifiers label 47.0% of unsafe native-script prompts as Safe, rising to 63.4% under romanization, while response-level safety accuracy drops by 10.3 points. Three multilingual guard models also pass 30.3-57.3% of unsafe native-script prompts. Using a model's own classification as an input filter reduces harmful compliance by only about five points. In contrast, BanglaShield, a compact fine-tuned encoder,…

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  • arXiv cs.AIOct 16

    SteerSearch: Interactive Search Reasoning with Mid-Response Steering

    Udit Thakur, Baohao Liao, Matteo Nulli, Stefan Vasilev, Seyyed Hadi Hashemi +2

    Search-augmented language models increasingly serve as long-horizon research agents, yet most open training methods assume a fixed user goal throughout search. We study mid-response steering, where users redirect a model during ongoing reasoning or retrieval. We identify answer-target anchoring, a failure mode in which fixed-query search agents retrieve evidence for the updated request but still answer an earlier question. We introduce SteerSearch, an RL framework that trains on interactive search trajectories with injected interventions to enable context-preserving target reassignment, and SteerSearch-Bench, a benchmark adapted from multi-hop QA datasets. We also investigate Contrastive Context Reward (CCR), an auxiliary signal for aligning post-intervention reasoning with the updated goal. Without CCR, SteerSearch improves average steering accuracy over Search-R1 from 11.1% to 31.3% on Qwen2.5-3B and from 36.6% to 44.6% on Qwen3-4B, while preserving standard single-query performance. Gains also extend to larger Qwen2.5 models and natural multi-turn conversations.

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  • arXiv cs.CVOct 16

    ULIFLOW: Unified Latent Human-Object Interaction Flow

    Wenqi Cai, Chunzhi Gu, Weichen Zhou, Yawen Zou, Yijin Wei +3

    Text-conditioned 3D human--object interaction (HOI) generation remains challenging because individually plausible human and object motions do not necessarily form a coherent interaction. We argue that a key limitation lies in conventional skeleton-based representations, which compactly describe body kinematics while leaving surface-level interaction geometry largely implicit, often requiring additional contact-specific regularizers. We propose ULIFlow, a framework that explicitly models continuous human--object relations within a unified latent space. To this end, we derive continuous marker--object relations from sparse body-surface markers and treat them as an explicit interaction modality alongside human motion and object dynamics. A Mixture-of-Transformers (MoT) autoencoder is then designed to jointly model these heterogeneous streams while preserving their modality-specific structures. Importantly, we let the relation stream directly participate in human and object reconstruction, encouraging the shared latent representation to preserve both individual dynamics and the interaction geometry that coordinates them. To further achieve variable-length generation, we introduce a…

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  • arXiv cs.LGOct 16

    Dataset Inference for Data Provenance and Privacy Auditing in Tabular Foundation Models

    Dariush Wahdany, Jesse Cresswell, Naiqing Guan, Atiyeh Ashari Ghomi, Franziska Boenisch +1

    Tabular foundation models (TFMs) are increasingly deployed through black-box APIs and trained on large collections of real-world tabular datasets. As this training paradigm becomes more widely adopted, private or proprietary datasets may be incorporated into pre-training corpora, yet there are currently no dedicated methods for determining whether a given tabular dataset was used to train a TFM. As a solution, we introduce the first dataset inference method for TFMs, aiming to infer whether a suspect dataset was part of a model's pre-training data. We systematically analyze a broad collection of candidate signals that can be observed from a black-box TFM via input manipulation and find that individual signals provide only weak evidence of training data membership. However, by combining multiple complementary signals, we can infer dataset membership for several state-of-the-art TFMs trained on real tabular data, achieving up to 0.99 ROCAUC dependent on the TFM. We then study factors that influence dataset identification, including pre-training data composition, model capacity, and the use of real vs. synthetic data. Our results show that our dataset inference method is a practical…

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Rising research topics

  • Training & fine-tuning46+155.6%
  • AI safety33+135.7%
  • Computer vision35+118.8%
  • Robotics21+90.9%
  • Evals66+57.1%
  • Code models14+40.0%
  • Small LLMs7+40.0%
  • Federated learning48+29.7%