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All fields3263Multimodal520Agents499Frontier LLMs431Inference & serving257Image segmentation1733D scene154Learning verifiable rewards151Graph learning148Voice AI97Classifier & causal discovery85Forecasting time77Evals77Flow matching69Teacher policy distillation49Federated 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.

Rising research topics

Coming with more sources: more tagged papers
  • Small LLMs10+400.0%
  • AI safety36+227.3%
  • Training & fine-tuning48+200.0%
  • Robotics24+200.0%
  • Computer vision37+164.3%
  • Image & video generation28+154.5%
  • Evals77+148.4%
  • RAG10+100.0%

Papers

  • arXiv cs.AIOct 17

    Comparing Sparsified MLPs and Transcoders: Many Transcoder Features Are Neurons in Disguise

    Stepan Shabalin, Mateusz Piotrowski

    MLP neurons in transformer language models are widely understood to be polysemantic, motivating transcoders - sparse, learned approximations of MLP layers - as interpretable replacement models for mechanistic analysis. However, recent work shows that MLP neurons can be used to recover sparse, interpretable circuits (Arora et al. 2026, Ye et al. 2025), raising the question of whether transcoders are necessary for circuit analysis. We define neuron features as transcoder features whose weights are strongly aligned with individual MLP neurons, and show that they account for a substantial fraction of active features in late layers across 13 open-source transcoders. Only neuron features have a stable correspondence with something in the underlying MLP: other features vary more between training runs, do not match the MLP's Jacobian, and change less significantly from their random initialization. Our findings reframe MLP neurons as a stronger basis for interpretability than commonly assumed, and call for better methods of attributing the non-neuron structure that is used by the model.

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  • arXiv cs.AIOct 17

    HABIT-Bench: Evaluating Habit Induction from Longitudinal Weak Evidence in Agent Memory

    Junming Zhang, Rubin Wei, Xu Cheng, Hao Sun, Jiarui Wang +5

    In long-term interactions with large language model (LLM) agents, users reveal habits through their choices and actions. These habits capture implicit user information that agents must infer across conversations. However, individual observations may provide only partial evidence about a habit or when it applies. Retaining past interactions alone does not ensure that agents can infer habits and apply them appropriately. We introduce HABIT-Bench to evaluate how well agent memory systems infer habits from weak evidence and apply them to later requests. We construct synthetic user histories from task-oriented dialogue data and controlled habit graphs. The histories span hundreds of sessions across food, finance, software, and travel. Four-choice tasks assess agents' ability to infer user habits and apply them appropriately. They include temporary exceptions, changes in habits, and suggestions that users have not accepted. Sampled tasks undergo independent human review. We evaluate eight memory methods with Qwen3-235B-A22B and GLM-5.3-Flash. The best-performing memory method achieves 33.61\% macro accuracy across the four domains. Performance remains limited even when annotated…

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  • arXiv cs.AIOct 17▲ 1

    Axiomatic World Modeling for Physics Reasoning

    Xinye Yang, Zhenyang Liu, Yuxuan Wang, Yuanyuan Lei

    Recent research-level physics-reasoning benchmarks for LLMs reveal a failure mode that lies not in knowledge or execution, but in modeling the world being reasoned about. Models often silently alter given quantities, fabricate laws, or inject unstated assumptions to force a problem into familiar patterns, and then reason inside this corrupted world. We define this premise-level divergence as modeling drift: the gap between a model's internal axiomatic world model (AWM) and the true physical world specified in the problem. Although RLVR and PRM have yielded substantial gains in mathematics and code, their reward shapes do not directly supervise world modeling in axiomatic domains such as physics. We propose Reinforcement Learning with Axiomatic World Modeling (RLAWM), which optimizes the policy by penalizing divergence between its inferred axiomatic world model and the true physics world. RLAWM probes physics reasoning at two hierarchical levels (modeling and solving), each scored by a physically grounded reward. The modeling reward targets abstract knowledge formulation, evaluating whether the model captures the true axiomatic world before attempting calculation. The solving…

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  • arXiv physics.opticsOct 17

    Enhancing analytical photon transport in turbid media through physics-guided neural network corrections

    Guojun Tang, Paul D. Teal, Alexander Doronin

    Analytical methods provide rapid and computationally efficient predictions of light transport in turbid media, making them attractive for real-time and inverse applications. However, their accuracy is limited by the approximations and assumptions used to obtain tractable solutions. On the other hand, Monte Carlo (MC) simulation is widely regarded as the "gold standard" for modeling light transport in turbid media, but its high computational cost limits its use in applications requiring repeated forward evaluations. Here, we introduce a physics-guided residual learning framework that combines the computational efficiency of analytical modeling with the accuracy of MC simulation. As a representative case study, we consider the diffusion approximation (DA), which can exhibit systematic errors under relatively strong absorption, weak scattering, and imperfect boundary treatment. Given the absorption coefficient, scattering coefficient, and anisotropy factor, the corresponding DA prediction is incorporated as a physical prior, while the network learns only the residual between the analytical prediction and the MC reference. Across the evaluated optical-property range, the proposed…

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  • arXiv cs.AIOct 17

    $M^2Net$: Unifying Scale-Specific Structures and Prototype Decomposition for Spatial-Temporal Forecasting

    Chengyang Zhou, Zijian Zhang, Hao Miao, Chunxu Zhang, Ziming Liu +3

    Spatial-temporal forecasting is pivotal for intelligent urban management yet remains challenging due to the intricate coupling of spatial dynamics and temporal non-stationarity. Existing approaches, while effective, often overlook two fundamental characteristics of real-world data: (1) Granularity-dependence, where dominant spatial correlations shift significantly across different time scales, and (2) Signal entanglement, where global common patterns are inextricably mixed with node-specific disturbances. In this paper, we propose a novel framework named $M^2Net$ (Multi-Scale Masked attention and Prototype network) to unify scale-specific structural learning and semantic decomposition. First, to capture the evolution of spatial dependencies, we introduce a Multi-Scale Masked attention mechanism. By downsampling temporal features into multiple resolutions, we dynamically generate scale-adaptive spatial masks, allowing the model to distinguish between micro-scale physical neighbors and macro-scale functional correlations. Second, to disentangle unique variations from shared trends, we propose a Prototype Residual Learning module. It retrieves global common patterns from a…

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  • arXiv cs.LGOct 17

    ProbePO: Sparse Probes, Redistributed Credit for Critic-Free LLM Reinforcement Learning

    Diyi Hu, Ruizhong Qiu, Lin Qiu, Hanghang Tong, Hanqing Zeng

    Reinforcement learning from verifiable rewards assigns one terminal reward to an entire reasoning trajectory, obscuring how individual reasoning steps contributed to the outcome. GRPO broadcasts a group-normalized advantage to every response token, while finer-grained approaches often rely on dense auxiliary rollouts or branching schemes that can also alter the data used for policy updates. We cast the missing middle as budgeted measurement: with a limited rollout budget, which partial solutions should be evaluated, and how should their value estimates refine credit? ProbePO selects partial-solution states with a reward-free uncertainty score and estimates their expected verifier rewards under a capped continuation budget. ProbePO redistributes, rather than replaces, GRPO's trajectory-level advantage across tokens while preserving each trajectory's mean token-level advantage; probe continuations serve only as measurements. Across three DeepSeekMath and Qwen-family models spanning 1.5B-7B parameters, ProbePO consistently outperforms GRPO and achieves the highest accuracy among the four evaluated methods in 16 of 18 model-benchmark comparisons. Against dense-probing VinePPO on…

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  • arXiv cs.LGOct 17

    TopoCompile: Molecular Generation with Exact Global Count Constraints

    Xiang Wu, Rong-Hua Li, Quanqing Xu, Kangfei Zhao, Guoren Wang

    Molecular generation designs novel molecules with desired structures and properties. Global count constraints specify molecular size, elemental composition, and ring content without prescribing detailed structure. Property-conditioned generators steer generation toward such targets but do not guarantee exact satisfaction. We introduce TopoCompile, which combines an autoregressive component-tree generator with a dynamic-programming compiler for supported component-additive integer counts. TopoCompile represents molecules as trees of intact ring systems and individual atoms, making each supported count additive over components. At each step, an autoregressive Transformer scores component and attachment actions, while the compiler masks actions with no target-satisfying completion. Within the fixed component vocabulary and typed attachment rules, constrained decoding guarantees exact equality for supported non-negative, component-additive integer counts. Applied at inference time, these checks let the same trained generator support different targets and count combinations without retraining. Across four benchmarks, TopoCompile achieves 100% Exact-valid yield for Common and Rare…

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  • arXiv cs.LGOct 17

    What "Form'' of Uncertainty Does Conformal Prediction Quantify?

    Soroush H. Zargarbashi, Mohammad Sadegh Akhondzadeh, Rebekka Burkholz, Aleksandar Bojchevski

    Conformal prediction (CP) is a post-hoc calibration method that returns prediction sets with distribution-free coverage guarantees, and the size of the output set is often interpreted as a notion of uncertainty. We show that the difference in set size between full and split CP is directly connected to Bayesian posterior variance, which is the epistemic uncertainty of the model. Essentially, this difference in set size is an aggregation of two opposite effects: (1) better model accuracy and therefore smaller set size achieved by training over extra calibration points, and (2) the "formability" of the model at test point towards every possible label. The latter effect vanishes with stronger models or more calibration data (as epistemic uncertainty is known for that behavior). It turns out that the model's formability itself (without the quantization effect of prediction sets, and through estimating the posterior variance) is a useful tool to quantify the epistemic uncertainty. In our empirical evaluation it performs on par with the state of the art in many setups.

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  • arXiv stat.MLOct 17

    Denoising Implicit Variational Inference

    Zitong Wang, Zekun Wu, Longlin Yu, Cheng Zhang

    Semi-implicit variational inference (SIVI) expands the representational capacity of variational families, but optimizing the Evidence Lower Bound (ELBO) remains challenging because the marginal score of the semi-implicit distribution is generally intractable. Unbiased Implicit Variational Inference (UIVI) and related methods address this difficulty through reparameterized ELBO gradients, but existing approaches estimate the required score indirectly through reverse-conditional sampling or approximation, which can be computationally costly and unstable. We propose Denoising Implicit Variational Inference (DIVI), which learns the marginal score directly via denoising score matching. The learned score is used as a plug-in estimator in the pathwise ELBO gradient, replacing inner reverse-conditional sampling with a score-network evaluation. We analyze DIVI as inexact stochastic gradient ascent and show, under stated assumptions, that the averaged stationarity measure is controlled by the usual optimization term and the average score-matching error. Empirically, we evaluate DIVI on both synthetic distributions and a variety of real-data Bayesian inference tasks. The results show that…

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  • arXiv cs.LGOct 17

    BEM: Learning Bregman Divergences for Concept Erasure in Flow Matching Models

    Mohamed-Hicham Leghettas, Markus Püschel

    Concept erasure removes harmful, private, or copyrighted concepts from text-to-image models while preserving their other abilities. Leading models such as FLUX and Stable Diffusion 3 are trained with flow matching, and erasure methods for them fine-tune the model toward a guided teacher's velocities under a Euclidean loss. Yet the squared distance is only one member of a larger family: Bregman divergences are exactly the losses under which flow matching recovers the correct velocity field, and each member shapes differently what is erased and what is preserved. We introduce BEM (Bregman erasure matching), which is, to our knowledge, the first to learn this divergence through bi-level optimization, so that the fine-tuned model forgets the concept on unseen prompts while its unrelated generations stay unchanged. In the experiments we target the suppression of nudity, violence, real identities, and copyrighted characters. Under the evaluation protocol of GEM, BEM removes more of the concept than GEM on most settings, cutting the unsafe rate on bloody gore from 53.9% to 14.4% and the FID on nudity from 8.64 to 6.64, while retention holds on some concepts and degrades on others.

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  • arXiv cs.ROOct 17

    GazeAct: A Human-Gaze-Annotated Manipulation Dataset for Attention-Regularized Policies

    Anupam Pani, Wang Yuan, Qifan Wang, Yanchao Yang

    Vision-language-action (VLA) policies must ground language instructions in the right objects, contact points, and target locations for reliable manipulation. Human gaze provides a natural signal for this grounding, but existing robot datasets rarely pair real human gaze with the multi-camera observations used by modern policies. We introduce GazeAct, a gaze-equipped real-robot manipulation dataset and training framework for multi-view VLA policies. Using screen-based teleoperation, we record gaze directly on the robot camera feed viewed by the operator, yielding 12000 demonstrations across 40 tasks and five domains with 300 Hz gaze synchronized with images, proprioception, and actions. We transfer gaze from the operator-selected view to the remaining synchronized cameras, producing spatially consistent targets across the policy's visual inputs. We then use these gaze-derived targets to regularize language-conditioned visual attention during training while keeping inference gaze-free. Across 18 real-world tasks, our method improves average success from 48.1% to 58.6%, with gains up to 25 percentage points. Ablations show that gaze works best as soft supervision and that real human…

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  • arXiv cs.CVOct 17

    SAP-Depth: Scene-Adaptive Probability Framework for Self-Supervised Monocular Depth Estimation

    Bowen He, Li Zhang, Yujie Chen, Chang Cheng

    Self-supervised monocular depth estimation remains challenging due to the loss of fine-grained details and limited adaptability to varying scene structures. In this paper, we propose SAP-Depth, a novel architecture based on Scene-Adaptive Probabilistic. Unlike traditional methods, we introduce a Dynamic Bins mechanism powered by a Learnable Parameter Predictor, which adaptively estimates scene-specific depth distributions conditioned on the input image context. Furthermore, a Bin-wise Probability Distribution Decoder is designed to effectively fuse multi-scale representations to predict dense depth probability distributions, significantly enhancing boundary sharpness and structural recovery. Through the collaborative coupling of these proposed modules, our model achieves dynamic prediction of scene-specific depth distributions. Extensive experiments on the KITTI dataset show that SAP-Depth outperforms existing state-of-the-art methods in accuracy, particularly in its ability to better preserve complex edge details. Further, tests on the Make3D dataset validate the model's excellent generalization capability and robustness.

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  • arXiv cs.LGOct 17

    HiPPO Functional Memory: Coordinate-Queryable Attention for Irregular Time Series

    Zhaodong Guo, Haoyun Feng

    Irregular time series require models to answer temporal queries at arbitrary coordinates, yet existing approaches typically retain observation-dependent representations that must be repeatedly resolved for each query. We introduce HiPPO Functional Memory (HiPPO-FM), a functional-memory attention operator that compiles an irregular history into a reusable coordinate-addressable state. Instead of storing observation-indexed tokens or latent trajectories, HiPPO-FM writes observations into a fixed-rank functional space and performs direct coordinate evaluation through matched functional geometry. Kernel-smoothed HiPPO coordinates and Gram-based readout define a shared write/read mechanism, while observed-support self-attention enables content-dependent representations before compression. We characterize the induced finite-dimensional query space and derive an error decomposition separating functional geometry approximation from learned-memory mismatch. Across continuous-coordinate reconstruction, irregular representation learning, real-world extrapolation, and event-sequence prediction, HiPPO-FM consistently improves strong irregular time-series baselines, achieving strong…

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  • arXiv cs.CVOct 17

    Investigating Length Bias and Robustness in LVLMs for Multiple-Choice Question Answering

    Md. Atabuzzaman, Hani Alomari, Chris Thomas

    Large Vision-Language Models (LVLMs) demonstrate strong performance on multiple-choice question answering (MCQA), yet their reliability under biased conditions remains underexplored. We investigate length bias, the systematic preference for longer answer options, through controlled benchmarks spanning fine-grained visual domains. LVLMs consistently favor longer options even when visually inconsistent, with accuracy dropping 2--38\% when correct answers are shorter and increasing 1--70\% when longer. This bias persists across model scales. Through layer-wise mechanistic analysis, we reveal that length bias emerges from late-layer suppression of visual evidence in favor of language-side length priors. Blurry-image experiments show that bias intensifies when visual evidence weakens, while abstention tests reveal that LVLMs struggle to refuse incorrect predictions. We propose and evaluate multiple inference-time mitigation strategies; while these provide partial improvements, none achieves deployable bias mitigation without relying on oracle information unavailable at inference time. These results underscore that inference-time interventions are insufficient, pointing to the need for…

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  • arXiv cs.LGOct 17

    TideKV: Delayed KV Eviction via Layer-Wise Chunked Prefill for Memory-Efficient Long-Context LLM Inference

    Yu Wang, Zhu Wang, Ning Hu, Yifan Guo, Xiu Ma +2

    Long-context large language model (LLM) inference is important for modern intelligent applications but remains constrained by peak memory during prompt prefill. Existing KV cache compression reduces decoding memory but typically requires full-prompt prefill to preserve global context, retaining a high construction peak. Online chunk-level eviction lowers memory earlier but selects before each layer has observed the complete prompt, potentially degrading cache construction and decoding. We present TideKV, an on-GPU cache-construction architecture using layer-major delayed eviction. TideKV processes all prompt chunks within each Transformer layer, retains full-prompt KV only for the active layer, and evicts at layer end. This schedule preserves full-prompt-aware selection while reducing activation and temporary memory. Experiments across multiple computing platforms show that TideKV preserves the inference quality and final cache size of the evaluated compression methods. At 64K on A100, it reduces the construction-time transient footprint by 83.89%. On RTX 4090, it increases time to first token by only 1.06% at 64K and enables 128K prefill. TideKV also completes 128K on Jetson,…

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  • arXiv cs.LGOct 17

    CoTs remain monitorable when being unfaithful requires computation: Replicating and Extending the Necessity Effect Across Eleven Models

    Arav Dhoot, Yixiong Hao, Zephaniah Roe

    While a LLM's chain of thought (CoT) is a promising approach to interpreting model outputs, prior work shows CoT's content does not always accurately reflect the reasoning process. Emmons et al. (2025) finds that models can follow a question hint without revealing so in its chain of thought, but when the hint is sufficiently complex the computation is forced into the CoT. This claim, however, has only been tested on Gemini models, and the datasets and code is closed source. We replicate their proxy task across eleven models from six families and find the necessity effect generalizes. Every model follows simple cues above a no-hint baseline, while concealed complex-cue following falls back near baseline for most models. We also extend the study from Emmons et al. (2025) to measure CoT-necessity and unfaithfulness across five cue families (four of them new) and three difficulty levels. The relationship between when CoT is necessary and unfaithfulness is task-specific and model-specific. For some hint types, models often verbalize their hint-following behavior even when the hint can be decoded in a single forward pass. These results support the case for CoT monitoring when unsafe…

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  • arXiv cs.CVOct 17

    CodecMotion-OAD: Efficient Online Action Detection with Compressed-Domain Motion

    Zepeng Sun, Naichuan Zheng, Hailun Xia, Junjie Wu

    Online action detection (OAD) identifies ongoing actions in streaming videos. Many existing OAD systems combine RGB appearance with optical flow, but dense-flow extraction remains a major computational bottleneck. Codec motion vectors (MVs) are available during decoding, avoiding a separate optical-flow computation. On THUMOS'14, optical flow exceeds MVs by 11.2 mAP points with motion-only inputs, whereas the gap narrows to 3.7 points after RGB fusion, with the largest reduction in action-category discrimination. We propose CodecMotion-OAD, which replaces optical flow with decoder-provided MVs while retaining existing temporal detector architectures. Its two-stage training first aligns MV features with a pretrained flow-feature interface without action labels and then trains RGB+MV detectors using ground-truth labels and soft predictions from a frozen RGB+Flow teacher. To the best of our knowledge, this is the first application of codec MVs to frame-level OAD. On THUMOS'14 with MiniROAD, CodecMotion-OAD achieves 68.3 mAP versus 70.9 mAP for matched-distillation RGB+Flow, while reducing decoder-to-prediction processing time by 11.48-fold on CPU and 2.04-fold on GPU.

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  • arXiv cs.AIOct 17

    ConCI: A Controlled Counterfactual Benchmark for Contextual Integrity in LLMs

    Juhyeon Park, Sumin Yu, Taesup Moon

    Contextual integrity (CI) holds that the appropriateness of sharing personal information depends on the context of the information flow, not on the information alone. However, existing CI benchmarks often use independent scenarios that vary multiple CI parameters simultaneously and are dominated by inappropriate flows, making it unclear whether correct predictions reflect sensitivity to context or a general refusal tendency. We present ConCI, a controlled-counterfactual benchmark in which an anchor flow is paired with two counterfactuals that each modify a single CI parameter. The resulting triplet is scored jointly, with credit given only when all three flows are judged correctly. Built from six existing benchmarks, ConCI contains 1,592 flows in 556 triplets, is approximately label-balanced, and is validated by seven human annotators per flow. Across seven LLMs, many correct anchor predictions fail on at least one counterfactual, and even the strongest model errs on about a fifth of triplets. Furthermore, models perform near ceiling on inappropriate flows but weaker on appropriate ones, often continuing to judge a flow inappropriate even when a contextual change should make it…

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  • arXiv cs.CLOct 17

    Backend-Frontend Converter: Component-Based Conversion of .NET Computational Models to TypeScript

    Petr Ivankov, Max Shestov

    Computational models developed in .NET often need to run locally within web applications. We present Backend--Frontend Converter, an architecture for generating TypeScript implementations of component-based models. The converter maps components, connections, aliases, and formula trees to model-specific TypeScript code. Reusable runtime libraries provide numerical integration, data access, and state handling. We evaluate stored generated implementations through four orbital scenarios and a recorded trading-strategy comparison. Orbital calculations execute in a browser and are paired with C\# results by crossing event and timestamp; trading outputs are aligned by date. Analytic scheduler tests and deliberately perturbed outputs assess the validation procedure. Each orbital scenario produces 91 paired events with identical timestamps. Position and velocity differences remain below $(10^{-6}\)~km$ and $(10^{-9}\)~km/s$. All 20 analytic scheduler checks pass, and the comparator detects each of three prescribed state and time perturbations. The trading comparison contains 501 aligned daily records with matching indicator values, positions, and order events. The evaluated…

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  • arXiv cs.LGOct 17

    VBO-MI: A Differentiable Black-Box Optimization Framework via Variational Mutual Information Estimation

    Farhad Mirkarimi

    Many real-world tasks require optimizing expensive black-box functions accessible only through noisy evaluations, a setting commonly addressed with Bayesian optimization. While Gaussian processes(GPs) and Bayesian neural networks have driven significant progress, GP-based methods scale cubically with observations, and BNN-based approaches remain burdened by expensive posterior sampling such as Hamiltonian Monte Carlo, and inner-loop acquisition optimization in high-dimensional setting. In this work, we propose VBO-MI, a fully gradient-based BO framework that requires no assumptions on a Gaussian prior or a variational posterior family over $f$. While our framework uses gradient-based updates to train the internal action and information-estimation networks, it treats the objective function $f$ as a strict black box, requiring only zeroth-order evaluations. To enable end-to-end gradient flow, we employ an actor-critic architecture consisting of an action-net to navigate the input space and a variational critic to estimate a surrogate information signal for exploration. Additionally, we design a lightweight surrogate that approximates the objective from observed data using a replay…

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