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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.

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%

Papers

  • arXiv cs.LGOct 17

    Revisiting Smoothed Online Learning: Competitive Ratio, Dynamic Regret with Switching Cost, and Best of Both Worlds

    Lijun Zhang, Linzhe He, Wei Jiang, Shiyin Lu

    We revisit smoothed online learning and develop algorithms with improved competitive ratios, optimal dynamic regret with switching cost, and best-of-both-worlds guarantees. Surprisingly, minimizing the hitting cost alone is $\max(1,\frac{2}α)$-competitive for $α$-polyhedral functions under the $\ell_2$-norm switching cost and $1+\frac{4}λ$-competitive for $λ$-quadratic growth functions under the squared $\ell_2$-norm switching cost, significantly improving existing guarantees. The polyhedral bound is tight for this naive algorithm, and we establish a matching lower bound for $α\geq1$. For convex $λ$-quadratic growth hitting costs, minimizing a suitably weighted sum of hitting and squared $\ell_2$-norm switching costs yields a competitive ratio of $1+\frac{2}{\sqrtλ}$. For dynamic regret with the $\ell_2$-norm switching cost, we develop Smoothed Ader (SAder), which achieves the optimal $O(\sqrt{T(1+P_T)})$ rate in the standard online convex optimization setting, where $P_T$ is the comparator's path-length. Its lookahead version removes the bounded-gradient assumption, and a matching lower bound establishes optimality in this setting. Finally, for convex hitting costs on a bounded…

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  • arXiv cs.LOOct 17

    Tactix: Towards a Programmable Strategy Interface for Flexible SAT Solving

    Haoze Wu, André Schidler, Armin Biere, Nina Narodytska

    Modern SAT solvers combine a rich repertoire of proof rules with sophisticated strategies that determine when and how to apply them. Often, the internal implementations are tightly coupled to particular strategies, making it inconvenient to define new strategies without changing solver internals. We present Tactix, a proof-producing sequential CDCL solver that incorporates a wide range of techniques in state-of-the-art solvers with a clean separation of proof rules from solving strategies. This separation enables a public interface through which users can invoke particular techniques, observe their effects, and program a wide range of solving strategies. The default strategy of Tactix is programmable over this interface and outperforms state-of-the-art solvers on benchmarks from the 2024--2026 SAT Competitions. We also use the interface to develop novel strategies tailored to challenging benchmarks from two application domains, which led to performance gains over strong baselines. We intend Tactix to serve as an infrastructure for designing and evaluating novel solving strategies. Finally, we also present a self-study of the AI-assisted development process behind Tactix.

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  • arXiv cs.AIOct 17

    WAQS: Weight Absorption for Quadratic Probing and Affine Steering

    Ding Zhu, Mohammad Mahdi Khalili

    Activation steering provides a lightweight alternative to fine-tuning for controlling large language model behavior. Existing methods, however, often face a trade-off between efficiency and expressiveness: fixed steering vectors incur negligible inference overhead but cannot adapt to the current model input, whereas input-dependent nonlinear interventions provide greater flexibility at additional runtime cost. We introduce \textbf{WAQS} (\textbf{W}eight \textbf{A}bsorption for \textbf{Q}uadratic probing and affine \textbf{S}teering), a framework that obtains state-dependent steering, without additional inference-time computation. WAQS models a concept with a quadratic probe, whose gradient yields an affine, activation-dependent steering direction. We show that this affine intervention can be folded exactly into adjacent linear transformations through an offline weight update. Moreover, we prove that quadratic functions form the maximal class of smooth concept scores whose gradient-based interventions are affine and therefore exactly absorbable into linear layers. Under a Gaussian representation model, our formulation admits an interpretation as the difference of class-conditional…

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  • arXiv cs.AIOct 17

    A Two-Stage Hybrid Framework for Inferring Air Trips from Mobile Location-based Service (LBS) Data under Incomplete Airport Sightings

    Hsuan-Chih Wang, Xianfeng Terry Yang

    Mobile device location-based service (LBS) data provide large-scale spatiotemporal observations that can help airport planners understand air travelers' mobility patterns. However, airport sightings are often missing before or after flights, making strictly rule-based detection incomplete. This study develops a two-stage framework to infer U.S. domestic air trips from large-scale LBS data under incomplete airport sightings. Stage 1 uses heuristic rules to identify high-confidence air trips whose endpoints are observed at two distinct airports. Stage 2 addresses latent air trips with a probabilistic model that infers air routes using spatial proximity, temporal compatibility, and historical flight schedule information. Using January 2022 nationwide LBS data with nearly 10 million users and 131 million sightings, results are validated against T-100 Domestic Segment route benchmarks and a Stage 1 pseudo-ground-truth reference set. The framework increases air route coverage from 10.65% under Stage 1 to 50.60% and recovers nearly all top-1,000 benchmark routes. It improves Spearman rank correlation with T-100 route shares from 0.4490 to 0.7630 and reduces Jensen-Shannon divergence…

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  • arXiv cs.CLOct 17

    FlashMap: Flow Map Speculative Decoding

    Jeff Shen, Zhejian Zhou, Ryan Lee, Jonathan May, Shirley Ho

    Speculative decoding accelerates large language model (LLM) inference by using a lightweight drafter to propose a block of tokens that the target model verifies in one parallel pass. Autoregressive drafters generate this block one token at a time, whereas block diffusion drafters such as DFlash generate it in one pass, making drafting faster. However, these diffusion drafters predict each token independently, ignoring dependencies between tokens in the block. We introduce FlashMap, a continuous flow drafter that conditions a flow map language model on the target model's hidden states and generates the whole block in one forward pass while capturing inter-token dependencies. On Qwen3-8B, FlashMap is faster than a matched DFlash baseline on every benchmark, with 7% higher average speedup and up to 6.1x lossless acceleration.

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  • arXiv cs.HCOct 17

    GazeAgent: VLM-Guided Implicit Gaze Calibration from Egocentric Multimodal Behavior for AI Glasses

    Yuhong Zhang, Khanh An Nguyen, Xinyu Tian, KungYi Chang, Abraham Padua +4

    AI glasses typically require explicit, user-specific eye-tracker calibration, and accuracy can drift when the device shifts during use. We present GazeAgent, a VLM-guided framework that continuously and adaptively improves gaze-calibration accuracy from naturally occurring behavior without asking users to interrupt their activities. GazeAgent identifies precise visual attention (PVA) events from multimodal streams of inertial measurement unit (IMU), gaze, and wearer-hand signals, validates their task relevance, and localizes functional targets as pseudo-calibration targets. In a 10-participant scripted study, the system detected all 820 predefined PVA opportunities and, under device disturbance, reduced median gaze error by 44.1% compared with explicit calibration. In a five-participant case study, autonomous calibration produced the lowest workload and interruption ratings, and all participants preferred it for routine use. By deriving task-critical targets from natural multimodal behavior, GazeAgent demonstrates the potential for continuous, adaptive, and low-burden gaze interaction on egocentric AI glasses.

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  • arXiv cs.CVOct 17

    Hexagonal Basis Projectors for Model-Based Iterative CT Reconstruction

    Ke Chen, Alireza Entezari

    Image representation affects both the forward model and quantitative feature recovery in model-based iterative CT reconstruction. For isotropically bandlimited signals, the hexagonal lattice is the most sampling-efficient lattice in two dimensions, with greater directional isotropy and a larger alias-free bandwidth than the Cartesian grid at equal density. We develop constant and linear hexagonal projectors for two-dimensional fan-beam CT using box spline calculus. Both bases form a partition of unity on common coefficient centers; the linear tent also reproduces affine functions. We derive analytic ray footprints and effective detector-blur expressions and implement efficient GPU forward and backprojection. On simulated FORBILD data, the linear model improves on Cartesian and hexagonal pixels, with its best noisy-data accuracy at the coarser resolution. Real-world 2DeteCT comparisons use high-dose, high-resolution Cartesian reconstructions as pseudo ground truth and show competitive image and feature agreement. These results establish a practical hexagonal representation for quantitative CT.

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  • arXiv cs.CROct 17

    Can Agents Keep Secrets They Were Never Told? Benchmarking Latent Privacy Risks Across Sessions with MemJoin

    Qishun Yang, Shu Yang, Difei Xu, Di Wang, Meng Ding

    To support sustained interactions and personalized assistance, agents use long-term memory to retain and combine user information across sessions. Reusing such memories can lead an agent to state a sensitive relation while completing an ordinary task that does not require it. The relation to protect and the combination of clues that reveal it may remain hidden at memory write time, when later records and tasks are still unknown. In this paper, we introduce MemJoin, a privacy benchmark of 288 synthetic analysis cases in which the complete history entails a sensitive relation, but every individual session is insufficient. We evaluate six memory systems with the same answer-generating model, alongside full-history and no-history controls. Each system writes and updates memories in session order and later retrieves context for an ordinary task, while target annotations remain with the evaluator. We separately check which target attribute values appear in stored records and retrieved context, and whether the final answer states the complete, person-linked relation. In a separate paired test on a prespecified subset, removing shared identifiers and other connecting clues can change…

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  • arXiv cs.AIOct 17

    DKnown-Jev: Fine-Tuning and Evaluation of a JEV-Style Model for Tasks Involving Normative Knowledge

    Qi Li, Jianjun Xu, Pingtao Wei

    Tasks involving normative knowledge include concept interpretation, eligibility determination, procedure explanation, material requirements, and service-outcome assessment, all of which require rules and facts published, maintained, or recorded by authorized entities. Whether a candidate paragraph can answer a question therefore depends on applicability and answer completeness as well as topical relevance. We develop DKnown-Jev to classify the normative usability of candidate paragraphs into two classes. The 2B and 27B configurations are fine-tuned directly from the native Qwen3.5-2B and Qwen3.8-27B base models, respectively. The models combine question conditions, article metadata, and paragraph content, adopt the classes Answer Evidence (AE) and Non-answer Evidence (NE), and undergo domain adaptation using human-verified training data. Five configurations are evaluated on the same test set of 1,120 human-verified examples. DKnown-Jev 27B trained for one epoch achieves the highest accuracy of 98.75% and macro-F1 of 98.60%, exceeding Jev by 6.07 and 6.76 percentage points, respectively. DKnown-Jev 2B trained for one epoch achieves 92.86% accuracy and 92.18% macro-F1, the best…

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  • arXiv cs.DCOct 17

    Koi: Evidence-Guided, Self-Calibrating Planning for Multi-Cloud, Heterogeneous LLM Inference Fleets

    Mankeerat Sidhu, Gangmuk Lim, Hetarth Chopra, Maximillian Eisenberg, Quan Hao Ng +8

    Deploying a large language model (LLM) requires choosing the right accelerator, parallelization strategy, engine configuration, and replica count amongst ~140 other variables across clouds, regions, and markets. These decisions are intertwined, and the best cluster-wide plan continually changes as jobs contend for shared capacity on shifting workloads. We present Koi, a self-calibrating cluster planner for heterogeneous, multi-cloud fleets. Koi uses a Recursive Language Model (RLM) hierarchy to propose hypotheses and reason over deployment while deterministic components predict serving behavior, score candidates, and control placement. It represents inference behavior as a fixed causal graph linking decisions (e.g., accelerator, parallelism), runtime mediators (e.g., KV-cache pressure, communication overhead), and outcomes (e.g., latency, throughput). Each deployment strengthens or weakens scoped causal relationships, building a comprehensive playbook for inference that the planner employs to guide future decisions. Job-wise configurations are composed into cluster-wide plans and evaluated against serving objectives, resource constraints, SLO risks, and cost. Across benchmarks,…

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  • arXiv cs.AIOct 17

    FlashRT-PAI: Fast Kernels Are Not Fast Pipelines for Physical AI Inference

    Liang Su, David Chen, Jason Li, Kevin Guo, Teresa Lv

    Vision-language-action (VLA), video-action (VA), and world action models (WAMs) form the backbone of modern physical AI systems, yet their real-time single-batch inference remains a defining bottleneck. Latency is dominated not by individual kernel computation, but by system-level overhead: thousands of short kernel launches, intermediate data movement across operator boundaries, and redundant recomputation across consecutive decisions. General-purpose graph compilers cannot address these costs, as they optimize individual calls in isolation and lack visibility into cross-call state semantics and global pipeline structure. We present FLASHRT-PAI, a low-latency inference engine built around model-hardware pipeline co-design rather than isolated kernel optimization. FLASHRT-PAI combines three core mechanisms: deep kernel fusion with launch overlap eliminates inter-operator boundaries and launch gaps; an execution contract formalizes the pipeline's stage DAG, update rates, and named buffers to enable cross-decision state reuse and selective refresh; and execution-state capsules enable fast snapshot, restore, and fork of persistent pipeline state. The system also supports drop-in…

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  • arXiv cs.NIOct 17

    Blackbox Performance Analysis of LLM-based Network Control

    Mahdi Alizadeh, Pooria Namyar, Arvin Ghavidel, Ramesh Govindan

    LLMs are becoming an indispensable part of network control systems due to their impressive reasoning. Before such LLM-enabled systems can be deployed, it is imperative to develop tools that can audit their performance on previously unseen inputs. However, auditing these systems is challenging due to the inherent opacity of LLMs. LENS is the first step towards black-box performance analysis of LLM-enabled network control systems. It uses novel methods to extend deep reinforcement learning to guide the search for performance gaps and inputs that make LLMs struggle. LENS finds large gaps for LLM-based traffic engineering and GPU allocation systems, and even matches existing white-box analyzers designed for hand-crafted TE heuristics.

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  • arXiv cs.LGOct 17

    Exact Risk Certificates for Pairwise-Judged Reranking

    Xinyu Wang, Jingrui Tian, Sicheng Lyu, Yu Gu, Ziyang Song +2

    Pairwise relevance judgments constrain both the loss numerator and the ideal-ranking denominator of nDCG. We characterize the exact worst-case loss of replacing a ranking with a fixed proposal. Binary gains attain this loss and provide an inspectable witness. Threshold verification reduces to at most $K+1$ maximum-closure problems. Our exposure-flow ablation isolates denominator coupling: on shared judge trajectories, exact verification saves 2.34 comparisons per TREC-DL development query and 1.98 per confirmation query at a budget of 32. Savings transfer to BEIR and a second proposer. Predictive gates achieve higher deployment gains, while certificates expose the worst gains still permitted by accepted comparisons.

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  • arXiv cs.LGOct 17

    Measuring the Task Value of Precision Upgrades in Closed-Loop Agents

    Sicheng Lyu, Xinyu Wang, Yu Gu, Lei Ding, Ziyang Song +1

    A low-precision agent can call its full-precision (BF16) model at selected decisions as a fallback. A learned precision router chooses where to call it from the disagreement between the two models and is judged by end-to-end success. An inserted BF16 action, however, can rescue a failing task or damage a succeeding one. We argue that a fallback's value is the number of tasks it saves minus the number it spoils, measured for the intervention that will actually run. From restored states, we run paired branches that cross which model supplies the current action with which model continues, separating replacement of one action, takeover of later execution, and both. On Qwen3 agents with simulated 4-bit weights in tau2-bench, replacing the first action where the two models differed in tool use changed many task outcomes, but rescues and damages nearly cancelled, and on held-out telecom tasks the action-only estimate was negative. Replacing both the action and the continuation had a positive net gain in two of five exploratory lower-bit stress settings. Two references changed outcomes at fixed bit width: replacing an action with that of a second 4-bit checkpoint changed about as many…

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  • arXiv cs.LGOct 17

    The Certification Exponent: Instance-Optimal Sequential Certification of Policy Improvement from History Dependent Logs

    Rui Wang

    Before a new policy replaces a deployed one, we often want a certificate, computed from logged episodes, that it is better. We ask how many episodes such a certificate needs when the logging policy is known but history-dependent, and the error probability must stay below $δ$ in every environment consistent with the logs. The answer is an exponent $K^\star$: the smallest KL divergence from the logged law to a law with the same logged action probabilities under which the target does not improve. No sound certifier stops in fewer than $\log(1/δ)/K^\star$ expected episodes, at any $δ$, and whenever $0<K^\star<\infty$ a plug-in Kelly certifier attains this rate to first order as $δ\to0$. Coverage, computed by one backward induction, decides whether certification is impossible ($K^\star=0$), possible at a finite rate, or possible in a constant number of episodes ($K^\star=\infty$). By convex duality, the optimal certificate bets on the importance-weighted advantage and also places fair bets on the logged actions, so doubly robust control variates act as hedges rather than estimators. As a consequence, no choice of off-policy estimator changes $K^\star$, a coverage ceiling explains the…

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  • arXiv cs.CLOct 17

    What Matters for NVFP4 Training? A Comparative Study of Low-Precision Pretraining Recipes

    Anjulie Agrusa, Andrei Panferov, Elizabeth Wei, Keith Wyss, Paul Gibbons +5

    Training large language models directly in 4-bit floating-point (FP) formats promises substantial improvements in throughput and energy efficiency. While some recipe design choices have been validated at scale, many promising approaches remain untested beyond small models and short token horizons, leaving open the question of which trends will hold. We present a systematic comparison of recent NVFP4 recipes at medium model scale, evaluating dense 8B and 30B-A3B MoE models trained for up to 1T tokens, with a focus on which ingredients are necessary for accuracy recovery. We propose a final recipe grounded in the principles behind established stable NVFP4 training at scale, incorporating state-of-the-art techniques such as unbiased gradient estimation with lower quantization error than stochastic rounding. To the best of our knowledge, this is the strongest FP4 training result demonstrated at this scale to date in loss gap to BF16. Through ablation studies, we find that: (i) each technique in the optimized recipe measurably improves loss trajectory, (ii) selective high-precision layers are necessary for recovering accuracy at scale, (iii) not all tensors in the backward pass…

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  • arXiv cs.LGOct 17

    Uniform convergence at optimal rate and scale

    Noga Alon, Steve Hanneke, Shay Moran, Tom Waknine

    Uniform convergence asks how many random samples suffice for empirical averages to approximate expectations simultaneously over an entire class of functions. It is a central concept in probability and a foundation of statistical learning. For binary-valued classes, the Vapnik--Chervonenkis theorem characterizes uniform convergence by finiteness of the VC dimension. For dimension $d$, the sharp worst-case sample complexity for additive error $\varepsilon$ is of order $d/\varepsilon^2$. For real-valued functions, the analogous combinatorial parameter is the fat-shattering dimension, which depends on an approximation scale $γ$. The challenge is to achieve error $γ+\varepsilon$ with order $d/\varepsilon^2$ samples, matching the sample bound for binary classes, where $d$ is now the fat-shattering dimension at scale $γ$. Previous results achieved either the optimal scale or the optimal sample complexity, but not both. Our main result closes this gap, establishing the sharp sample complexity at the optimal scale. Along the way, we use several tools that may be of independent interest. The first concerns $\varepsilon$-nets with margin. A classical $\varepsilon$-net intersects every set…

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  • arXiv cs.AIOct 17

    Screening LLM Judges Without Ground Truth: A Preliminary Evaluation of an AI Dental Hygiene Coach

    William DeShong, Barath Kannan, Auberon Palma, Brenda Bradshaw, James F. Leathrum, +1

    Evaluating a fine-tuned coaching model with expert graders is slow and expensive, and an LLM judge cannot be trusted without ground truth. We present a staged pipeline that screens candidate LLM judges and student models before expert review, using explicit decision rules at each stage: judge output audit, similarity with hosted judges, bias checks, and decision robustness. We demonstrate it on an AI dental hygiene coach. The case study uses synthetic, clinically unvalidated training and reference data to demonstrate the operation of the pipeline, rather than to establish the clinical performance of the resulting models. Two fine-tuned small language models (Granite-4.1-8B and Qwen3-8B), each trained at three data sizes, were scored on seven metrics by five judges (three local, two hosted). Two local judges, Qwen3.8-27B and Gemma-4-31B-it, were not distinguishable from the agreement between the hosted judges on six of seven metrics by rank correlation, and Qwen3.8-27B was the most similar to the hosted judges overall. No judge met our criterion for favoring its own model family or training-data source. The student comparison did not separate the two models on any metric under the…

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  • arXiv cs.LGOct 17

    Power-Law Correlations Without Apparent Temperature-Tuned Criticality in Fixed-Tree Hierarchical Languages

    Marcel Kühn, Bernd Rosenow

    Sampling temperature can substantially reshape autoregressive generation, but it is unclear when temperature changes in long-range correlations reflect a genuine phase transition rather than a smooth change in statistics. We study this question in a controlled analogue of temperature sampling: a finite-depth probabilistic context-free grammar on a fixed hierarchical tree, where exact next-token conditionals are computed by belief propagation and then rescaled by a softmax temperature before autoregressive sampling. We measure two-point mutual information between tokens as a function of separation. Across the tested temperatures and grammar ensembles, mutual information remains well described by a power-law envelope. Temperature changes the correlation magnitude and fitted exponent, but within the accessible system sizes we find no long-distance plateau characteristic of an ordered phase and no clear crossover to short-range exponential decay. An approximate mapping to an Ising model on a tree explains how exponential decay in tree distance becomes a power law in token distance, allowing power laws to persist without tuning to a special decoding temperature. This controlled…

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  • arXiv cs.LGOct 16

    PAC Learning with Query Feedback

    Chase Hutton, Adam Melrod, Han Shao

    We study realizable multiclass PAC learning with query feedback, where each training example provides a single binary response indicating whether its true label belongs to a chosen subset from a fixed query family. Our main contribution is the query dimension, a combinatorial parameter that captures both the structure of the hypothesis class and the ability of the available queries to distinguish labels. This parameter generalizes the bandit DS dimension, which it recovers under singleton queries. We establish a sample-complexity lower bound of $Ω(D/\varepsilon)$ and an upper bound of $\widetilde{O}(D^2/\varepsilon)$, suppressing factors polylogarithmic in the number of labels and the failure probability. When the available queries can split every candidate set into two parts of comparable size, we improve the upper bound to $\widetilde{O}(D/\varepsilon)$, yielding a characterization up to logarithmic factors.

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