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ByteTrail · AI and developer-tools intelligence
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Fields · this week
All fields2491Multimodal434Agents415Frontier LLMs380Inference & serving213Image segmentation147Graph learning1363D scene127Learning verifiable rewards112Voice AI67Classifier & causal discovery66Forecasting time63Flow matching56Teacher policy distillation46Adversarial training42Machine learned interatomic38All 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.

Rising research topics

  • Anomaly detectors30−11.8%
  • Adversarial training42−16.0%
  • Forecasting time63−18.2%
  • Federated learning37−19.6%
  • CAD models12−25.0%
  • Frontier LLMs380−25.2%
  • Inference & serving213−26.3%
  • 3D scene127−28.7%

Papers

  • arXiv cs.CLRAGOct 14

    Cascaded Retrieval with Cross-Modal Query Refinement for Efficient Visual Document Search

    Diana Turmakhan, Aseem Srivastava, Vivek Gupta, Iryna Gurevych, Thamar Solorio

    Retrieving information from visually rich documents requires matching queries against pages that contain complex evidence in tables, figures, and layout-dependent structures. Current vision-language retrievers address this but incur query-time costs that grow with corpus size and are impractical to scale. We propose SCOUT, a cascaded two-track retrieval framework that separates retrieval performance from exhaustive search. The visual track first uses a lightweight parent retriever to filter the corpus to a compact candidate pool, then applies a stronger multi-vector reranker only to those candidates. In parallel, a semantic track retrieves over hierarchical page and document summaries generated offline to provide a signal complementary to visual matching. The outputs are fused through cross-modal query refinement, which uses semantic scores to adjust the visual query representation before final ranking. We evaluate SCOUT on 4 major benchmarks against 6 baselines and observe a significant query-time latency improvement up to 80.7x (28.8x on average) relative to the best baseline, while exceeding state-of-the-art retrieval performance by up to 7.8% NDCG across all benchmarks.

    AIMultimodal80PDF
  • arXiv cs.CLAgentsOct 14

    Lexical Persistence Without Semantic Entrenchment in AI Agent Communities

    Chih-Han Lin, Yu-Yun Chang, Po-Ya Angela Wang, Shu-Kai Hsieh

    AI agent communities sustain lexical persistence, popular terms spread, recur, and achieve broad adoption, but whether they also develop the temporal semantic structure characteristic of human linguistic communities remains open. We compare MoltBook (AI agent community, 8 weeks), PTT (Chinese human BBS, 19 years), and Reddit (English human forum, in matched 8-week windows) using Compass-Aligned Distributional Embeddings (CADE), measuring semantic drift (B1), neighborhood stability (B2), and lag-1 autocorrelation of per-word drift trajectories. Across the platforms, lag-1 autocorrelation forms a memory-horizon continuum. AI agents' result clusters at the low end, while human platforms show longer-range temporal dependence. MoltBook exhibits approximately 2$\times$ the B1 drift and 20-30% lower B2 stability than both human platforms. As a partial control for community age, r/vibecoding (founded 2025-02-08) shows significant ACF in its first 8 weeks ($0.082$, $p<.001$) and over 15 weeks ($0.220$, $p<.001$); MoltBook's ACF remains non-significant ($p = 0.41$), consistent with the gap not reducing to developmental stage alone. We interpret this pattern as evidence that lexical…

    AI50PDF
  • arXiv cs.LGOct 14

    Fractional is Better: Learnable Derivative Orders in Neural Operator Learning

    Fares B. Mehouachi, Saif Eddin Jabari

    Neural operators learn mappings between function spaces, enabling fast surrogate solutions to partial differential equations. Despite remarkable architectural diversity, these methods often share a common input representation: raw coordinate-value pairs $(x, u(x))$. We ask whether inputs aligned with PDE differential structure can improve learning. Through Picard iteration on mild solutions, we show that derivatives of the input appear explicitly in the solution operator, suggesting that providing derivative features should reduce the network's implicit differentiation burden. We prove this intuition: providing derivative features improves approximation bounds from $O(W^{-(s-m)/d})$ to $O(W^{-s/d})$, where $W$ is parameter count, $s$ is input regularity, $m$ is the PDE order, and $d$ is spatial dimension. Our central finding, however, is a surprise: the optimal derivative order $β^*$ is strictly less than the PDE order $m$. This gap arises from a bias-variance tradeoff in spectral space that we characterize in closed form. Learning $β$ from data achieves automatic spectral regularization. We introduce $\partial$-NO (del-NO), for derivative-augmented neural operators, an…

    AI30PDF
  • arXiv cs.CLAgentsOct 14

    Do AI Agents Gossip? Evaluative Talk in Human and AI-Agent Online Communities

    Yong-Hui Hua, Chih-Han Lin, Shu-Kai Hsieh

    Gossip, defined as evaluative talk about absent third parties, is widely seen as a uniquely human social practice. This study examines whether comparable discourse emerges spontaneously in a community of autonomous AI agents. We compare Moltbook, a Reddit-style platform populated by AI agents, with PTT, a large human-operated bulletin board in Taiwan. To reduce differences attributable to communicative setting, we analyze two public, asynchronous forums using the same operational definition of gossip. Gossip occurs on Moltbook, but its prevalence is 2.6 to 6 times lower than on PTT, depending on the denominator used. On both platforms, gossip frequently targets locally prominent figures, tends to elicit agreement from the first respondent, and is rarely presented as unsupported rumor. The clearest difference concerns how subsequent responses develop. Among threads containing at least one codable reply, challenges occur in 65.1% of human threads but only 21.3% of agent threads. Moreover, following an early aligning response, consensus remains unchallenged in 83.6% of agent threads, compared with 44.7% of human threads. This thread-level contrast remains after controlling for…

    AI50PDF
  • arXiv cs.PLOct 14

    Typechecking without Minimum Types: Finite Bases and Backward Checking for $F_{<:}^{\top}$

    Mingkun Xiao, Yixuan Sun

    System $F_{<:}^{\top}$ has decidable subtyping but lacks minimum types. We prove that typechecking is decidable for arbitrary explicitly annotated terms with unrestricted subsumption. The difficulty is that type abstraction uses a variable's declared bound to type its body, while quantified subtyping forgets that bound. We construct finite covers that preserve all required subtype comparisons across this change of context. These covers yield a compositional finite typing basis and a decision procedure. We then derive a backward checker that propagates acceptable targets without constructing the complete typing basis. Rectangularity of typing families gives a uniform elimination domain while retaining every result type, even when the canonical type is not least. Finite inverse families transport targets through type substitution. The same inverse specification recovers bound forgetting and yields finite type-avoidance families. Both typechecking procedures are total Rocq functions proved equivalent to the original typing judgment. For a separate symbolic word-RAM realization, we prove a quartic worst-case work bound. We measure family representation sizes and comparison storage in…

    PDF
  • arXiv cs.LGInference & servingOct 14

    Beyond Expert Rankings: Contrast Calibration for Mixed-Precision MoE Quantization

    Renjie Xie, Juncheng Yang, Aoting Hu, Yan Liang, Mingxi Zhang +3

    Mixture-of-experts (MoE) language models activate only a few experts per token, yet all expert weights must be stored. Mixed-precision quantization reduces this storage cost by assigning higher bit-widths to sensitive experts and lower bit-widths to resilient ones, but expert rankings alone do not determine how strongly to weight their errors. To address this gap, we propose NatScale, a contrast-calibrated allocation framework for mixed-precision MoE quantization. It uses a model-specific power transformation to calibrate gradient-based expert weights to an information-theoretic contrast target while preserving rankings. We jointly evaluate each expert's gate, up and down configurations to estimate its output error. We then combine these errors and calibrated weights with an existing prior based on weight spectra, variance and precision decay to select configurations under a storage budget. Across three models, NatScale achieves the lowest native-to-quantized KL divergence and the highest top-token agreement in all nine settings against five allocation baselines. It also yields the lowest perplexity in seven settings. These findings highlight the importance of objective-aware…

    AIFrontier LLMs80PDF
  • arXiv cs.CVMultimodalOct 14

    ReferFlow: Beyond Final-Target Localization for Complex Referring Expressions

    Tianyi Gao, Han Fang, Xin Wei, Yaoyao Zhong, Zhaoyan Pan +9

    Multimodal Large Language Models have made significant progress in referring expression comprehension (REC), yet most REC benchmarks still focus on final-target localization. Complex queries often involve intermediate visual references with different dependencies on prior grounding, but final-only evaluation leaves this process unexamined. To address this gap, we formulate trajectory-level REC, in which models receive an ordered sequence of grounding steps constructed from a single complex query and localize the target regions associated with each step. We instantiate this setting in ReferFlow-Bench, which characterizes structured region transitions through four trajectory types, namely Refinement, Shift, Reset, and Mixture. Experiments show that current models struggle to consistently ground intermediate visual references across successive steps. We further propose ReferFlow, which decouples interleaved text-latent trajectory generation from coordinate decoding. Specifically, Stateful Region Latent Reasoning represents each target region as an object-indexed state composed of continuous latents, with explicit visual and spatial supervision to preserve region information for…

    AIEvals80PDF
  • arXiv cs.RORoboticsOct 14

    Human Demonstrations as Post-Training Data for Any Robot Body: Retargeting the Object, Not the Hand

    Nagasrikanth Kallakuri

    Human demonstrations are today used largely for pretraining vision-language-action models, with little ability to use the same data for target-embodiment or task-oriented training, which scalable deployment of robots in industrial and general environments depends on. A human demonstration recorded on a simple and scalable device such as a mobile phone can serve as post-training data for a robot whose body differs in structure and capability from the human who recorded it. This is made possible not by retargeting the hand's motion but the object's. A capture yields a body-free record: the object's pose over time, its contact intervals, and a reconstructed 3D object with provenance. Any robot, given as a URDF and a short descriptor, is solved against that record and scored in simulation against the demonstrated object motion as ground truth: the human-to-simulation leg of human-to-simulation-to-robot transfer, measured per robot. Each (robot, episode) pair gets one outcome: pass, failure with a named mode, or refusal, with its reason, before any motion. The method runs as a working system over a growing collection; the set analysed here is a sample of it: 42 captures solved on 8…

    AI80PDF
  • arXiv cs.AIAgentsOct 14

    Lifted State Policy Optimization: Internalizing Self-Correction through Transition Geometry

    Ren Zhuang, Ben Wang, Shuifa Sun

    Preference optimization ranks final answers and trajectory RL maximizes aggregate returns, yet neither credits the local change that turns a flawed reasoning state into a better one. We introduce Lifted State Policy Optimization (LSPO), which establishes this finite, cost-adjusted transition as the primary unit of learning. Each textual answer is augmented with a learned continuous coordinate, forming a lifted state that an energy function evaluates and orders. A shaped reward drives energy descent net of edit and step costs, and an internalization loss absorbs selected low-energy answers into the direct generator. On Qwen3.5-27B, the resulting single-pass policy reaches 71.8% aggregate accuracy across mathematics, science, code, and logic benchmarks, matching test-time reflection at 71.9% with 28% lower latency and 25.8% fewer tokens. The pattern holds across dense and mixture-of-experts backbones, and matched controls confirm that transition credit and the lifted coordinate each contribute, as does answer internalization. Our code is available at https://github.com/Venomrko/lspo.

    AIEvals70PDF
  • arXiv cs.LGOct 14

    NeuroDETR: Multiscale Discrete Spatiotemporal Tokenization for Asynchronous EEG Event Detection

    Yaozhi Wen, Kun Zhao, Wei Wei, Haoyang Chen, Kangning Wang +5

    Asynchronous EEG event detection supports practical brain-computer interfaces by identifying event classes and temporal boundaries directly from continuous recordings without predefined event boundaries at inference time. The primary challenge lies in extracting event-related patterns embedded in ongoing background activity. To address this, we propose \textbf{NeuroDETR}, a discrete spatiotemporal multiscale framework that combines structured tokenization with event-aware feature refinement. Multiscale temporal codebooks capture brief responses and sustained dynamics, while Spatial-Pyramid VQ hierarchically encodes spatial structure across EEG channels. These complementary representations form a compact event memory for jointly classifying events and estimating their onsets and offsets. Before tokenization, we apply FOCAL, an event-aware sparse attention module that uses predicted event presence to guide temporal feature refinement and spatial aggregation, emphasizing likely event periods while preserving context across the recording. Experiments on eight EEG datasets that span diverse paradigms demonstrate state-of-the-art performance. These findings provide a basis for the…

    AI30PDF
  • arXiv cs.LGOct 14

    Non-asymptotic Convergence of Average-reward Q-learning with Options

    Kintan Saha, Ahana Deb, Anders Jonsson, Debabrota Basu

    We study the finite-sample convergence of Q-learning algorithms for the average-reward formulation of Reinforcement Learning (RL) with options. While options allow temporarily extended and hierarchical actions, the average-reward formulation incorporates continual tasks beyond discounted and episodic RL. We establish the first $\tilde{O}(1/\text{iterations})$ convergence guarantees, in the mean-square sense, for average-reward option Q-learning algorithms in both the inter- and intra-option settings. First, we prove that both inter- and intra-option Bellman operators satisfy multi-step span contractions under unichain assumptions. Then, we propose synchronous estimators of these multi-step option Bellman operators leading to two variants of option Q-learning. Finally, we extend the stochastic approximation framework to prove convergence rates of these two algorithms under the minimal assumptions used in the literature.

    AI30PDF
  • arXiv cs.SEAgentsOct 14▲ 105

    Doc2Harness: Benchmarking LLMs on Long-Horizon Harness Engineering of Coding Agents

    Yuyao Ge, Shenghua Liu, Peizhong Gao, Chendi Ge, Chenxu Liu +6

    Agents driven by large language models are increasingly deployed on long-horizon tasks, where they use tools and modify real workspaces. How well they perform depends as much on the harness around the model as on the model itself. Existing benchmarks measure downstream task success under a fixed harness, leaving harness engineering itself unexplored. We propose Doc2Harness, a two-stage benchmark that evaluates long-horizon harness engineering directly. In Stage 1, a build model operating inside a fixed builder harness receives an interface-only contract skeleton and a development document that consolidates behavior across twelve frontier harnesses, and then constructs a complete candidate harness in an isolated container. In Stage 2, the frozen candidate harness runs in containers separate from the construction environment, one per test case, and is driven end to end by a scripted mock model. We score each test case using four observable channels, namely the requests sent to the model, the files on disk, the process exit code, and the command output; positive controls rule out vacuous passes. Across nineteen frontier models, each evaluated with three independent builds on 328…

    AIEvals90Code
  • arXiv cs.LGOct 14

    When Does Task-Aware Imputation Matter? An Analysis of Robustness Under Train-Test Mismatch

    Maria V. Salvador, Ana F. Sequeira, Jaime S. Cardoso

    Missing data is a pervasive challenge in machine learning, often degrading predictive performance and complicating model deployment. Most existing approaches treat imputation as a preprocessing step independent of the downstream task, a separation that becomes problematic under train-test mismatches in missingness mechanisms or rates. We introduce a unified framework that treats missing entries as variables in a task-conditioned optimization problem. We instantiate this framework in two settings: an amortized approach for scenarios with missing data at both training and test time, and a latent optimization approach for settings where only training data is incomplete. The evaluation is conducted on 98 tabular datasets, and robustness under train-test mismatches is assessed on 36 datasets. Results reveal that reconstruction error constitutes a poor proxy for predictive utility (Spearman rho approximately 0.16 to 0.32), and that task-aware imputation becomes critical in scenarios where classical methods fail to recover complete-data performance, achieving win rates of up to 80% in challenging train-test mismatch scenarios.

    AI20PDF
  • arXiv cs.CVComputer visionOct 14▲ 119

    InfiniSplat v2: Analyzing and Improving Feed-Forward 3DGS from Any Views

    Yongzhen Hu, Hao Yu, Jiawei Wang, Xianming Zeng, Jianyun Xu +5

    Feed-forward Gaussian reconstruction builds 3D scenes directly from input images for novel view rendering, but learning Gaussian attributes from predicted geometry for high-quality rendering under large viewpoint changes remains challenging. We analyze limitations in Gaussian attribute learning and introduce InfiniSplat v2, which improves rendering quality through architectural and training changes. Our method decouples geometry and attribute prediction with a frozen geometry branch, aligns predicted Gaussians using ground-truth camera poses during training, and adopts large-angle target view supervision. Across six datasets, the feed-forward outputs of the same trained model achieve state-of-the-art mean LPIPS and DISTS among the evaluated methods in both pose-conditioned and pose-free settings. In the pose-conditioned setting, mean LPIPS and DISTS decrease by 25.8% and 32.9%, respectively, compared with the strongest evaluated baselines. We further find that opacity learned with only image reconstruction supervision suppresses geometrically inconsistent points. Project page: https://zju3dv.github.io/InfiniSplat2/

    AIImage & video generation70Code
  • arXiv cs.LGOct 14

    Training Dynamics of Transformers on Unbounded Inputs: Attention Learning Can Lag Output Layer Learning

    Yaomengxi Han, Debarghya Ghoshdastidar

    The success of transformers is attributed to several factors, including their capacity to express formal languages over variable-length inputs, and ability of the attention mechanism to focus on relevant input features. However, there is limited understanding of the training dynamics of shallow transformers, specifically how to decouple the learning of the attention and the output (feedforward) layer. A natural extension of the theory of one-step gradient feature learning in feedforward networks suggests that one could first learn the attention weights with a single gradient step, and then train the output layer. We show that this fails for a shallow transformer trained to recognize a star-free language from unbounded-length input. We prove that the transformer can be trained to recognize the language with a three-phase gradient descent: the output layer is updated first, followed by an attention update, and the output layer is fine-tuned in the third phase. In contrast, the language cannot be learned by training only the attention or output layer, or by a single attention-layer update followed by finetuning the output layer. We empirically show that ``attention learning lagging…

    AI30PDF
  • arXiv cs.CVMultimodalOct 14

    Read, Parse, Describe: Unified Document Parsing with Visual Element Description Generation

    Yuheng Chen, Yufan Chen, Zhuojun Cai, Ruiping Liu, Junwei Zheng +6

    Document understanding systems are typically evaluated as separate components via layout analysis, optical text recognition, reading-order prediction, and image captioning. This separation leaves scientific figures, captions, and visual semantics weakly connected, and fails to measure whether a model can produce a single page-level parse that is both structurally faithful and semantically accessible. We introduce Read-Parse-Describe, a unified dataset for document parsing with visual element description generation. Given a page image, models are required to output an ordered list of layout elements containing bounding boxes, semantic labels, reading-order indices, recognized textual content, and alternative text descriptions for visual elements. The benchmark is built from 2,402 documents, comprising 18,163 continuous pages, 125,218 layout elements, and 11,338 visual instances. Besides, we further propose a plug-and-play space-awareness enhancer that reuses multi-layer visual features and injects grid-aware spatial guidance into vision-language tokens for layout-aware generation. Experiments across end-to-end VLMs and pipeline parsers show that existing systems still struggle to…

    AIEvals80PDF
  • arXiv cs.AIAgentsOct 14

    TreeFlex: Agents Navigating Their Own Search Trees

    Dongzhuo Li, Fangzhou Cheng, Weiwei Li, Kevin Han, Haixu Ma +6

    A ReAct-style agent accumulates observations, intermediate results, and failed attempts in a single context. As this context grows, stale and off-path information consumes its limited capacity and obscures relevant evidence, making focused reasoning increasingly difficult. We introduce TreeFlex, a framework in which the agent's own policy decides where to search. The agent records each intermediate state, with a short summary, as a node in a search tree maintained by the harness. The policy is a single language model that acts in two roles. In the Reflection role, it has a global view of the tree through these summaries and picks a node to resume, together with an instruction. In the Execution role, the harness restores the path that led to that node, and the model carries out the instruction with the usual task tools until it saves a new node or proposes a solution, which Reflection then approves or sends back to another node. Through these choices, the policy decides whether to deepen the current line, branch from or revisit an earlier state, verify a proposal, or stop, without a fixed traversal order or a score for each node. We train navigation, task actions, checkpointing,…

    AI80PDF
  • arXiv cs.LGImage & video generationOct 14

    Are We Overthinking One-Step Generation?

    Woobin Park, Jaeyeon Kim, Yilun Du, Sitan Chen, Bohyung Han

    Diffusion and flow matching are now dominant frameworks for generative modeling in continuous domains, but they typically require multiple network evaluations at inference. This has motivated extensive research on one-step generative modeling primarily built on diffusion and flow, though existing approaches often rely on complex training machinery. In this work, we introduce Priming Models, a simple two-stage framework for training one-step generators. Motivated by the difficulty of distributional training from scratch, Priming Models are first trained on a reconstruction objective, e.g., pixel-space denoising at a fixed noise level, before undergoing feature-space distributional post-training. Our central scientific finding is that this simple reconstruction stage is already enough to entirely alleviate the difficulty of distributional training. Empirically, across class-conditional image generation, text-to-image generation, and world modeling, Priming Models significantly advance the Pareto frontier of training compute vs. generation quality. On ImageNet at $256 \times 256$ resolution, we achieve a state-of-the-art FID of $1.19$ among one-step generators using approximately…

    AI80PDF
  • arXiv cs.AIEvalsOct 14

    Argument Graph Uncertainty: Quantifying Uncertainty from the Logical Structure of Reasoning Chains

    Simon Baur, Sumire Honda, Wojciech Samek, Jackie Ma

    Reliable uncertainty estimation is essential for deploying large language models in high-stakes reasoning tasks, yet most existing approaches rely on expensive sampling strategies or shallow token-level signals that ignore the internal logical structure of model outputs. We introduce Argument Graph Uncertainty, a post-hoc framework for multiple choice question tasks, that estimates model confidence directly from the logical structure of a single chain-of-thought reasoning trace. Our method uses either a lightweight instruction-tuned model or a simple deterministic rule to segment the reasoning chain into propositional units, then applies a NLI model to infer relational edges and construct two complementary argument graphs over these segments. The resulting edge scores are aggregated into a Dirichlet posterior over answer options, whose concentration yields calibrated uncertainty estimates reflecting both local step-to-step coherence and global logical consistency across the reasoning trace. Evaluated on GPQA, MMLU-Pro and TruthfulQA, our method consistently surpasses token-based and consistency-based uncertainty baselines across multiple reasoning models. We further demonstrate…

    AI60PDF
  • arXiv cs.CLVoice AIOct 14

    Anuvani-Diarization: Zero Shot Speaker Diarization and Annotation Conventions Across 22 Languages

    Stephen Fernandes, Narayan Choudhary

    India's 22 scheduled languages are spoken by over a billion people in conversations that mix languages, often within one turn, overlap freely, and take place over telephones, in meetings, and in media. Knowing who spoke when, the task of speaker diarization, is essential to speech applications for such conversation: voice agents conversing with several people at once, speaker attributed transcription of meetings and calls, contact center analytics, and media monitoring. We present a systematic study of speaker diarization across all 22 languages on IndicDiarBench and introduce Anuvani-Diarization, which adapts a self-supervised end-to-end segmentation model to Indian conversation with uncertainty aware silver supervision. Evaluated zero shot alongside nine recent diarization systems, it reaches a duration weighted diarization error rate (DER) of 16.78% on all 1,164 recordings, the lowest of all systems we ran and second only to the best published result (16.0%), and leads the three strongest rivals in 14 of 22 languages. It matches its parent on six international benchmarks. We also show that the benchmark's utterance level annotation convention is a hidden variable worth 1.9 to…

    AI80PDF
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