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ByteTrail · AI and developer-tools intelligence
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Fields · this week
All fields2967Multimodal505Agents503Frontier LLMs438Inference & serving253Image segmentation176Graph learning1593D scene154Learning verifiable rewards136Voice AI89Classifier & causal discovery83Forecasting time74Flow matching69Teacher policy distillation51Machine learned interatomic51Adversarial training48All 23 fields

Rising research topics

  • Federated learning45+4.7%
  • Adversarial training48−2.0%
  • Anomaly detectors33−5.7%
  • Forecasting time74−6.3%
  • Machine learned interatomic51−7.3%
  • Agents503−11.8%
  • 3D scene154−12.5%
  • Voice AI89−13.6%

Open problems across fields

  • Compositionality in RAG pipelines1 paper raises it · 1 addresses it

    Combining individually strong RAG components often fails to improve overall performance, and gains may not transfer across retrieval quality and answer faithfulness.

    • Retrieval Augmented Generation Compositionalityraises · addresses
  • Request execution in streaming video generation1 paper raises it · 0 address it

    Models struggle to reliably follow incremental multimodal requests while maintaining state consistency and visual fidelity.

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

    Evolving execution harnesses may demand behaviors the model has not learned or restrict existing capabilities.

    • CoEvo-RSI: When Harnesses Evolve, Which Models Should Follow?raises · 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 answer correctly without analyzing necessary audio-visual evidence, weakening reliability.

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

Named by an AI from these papers' abstracts.

Papers

  • arXiv cs.AIAI safetyOct 14

    Counterfactual Neighborhood Risk Fields: Evidence-Grounded Selective Safety for Vision-Language Models

    Tianshi Wang, Jingsong Wang, Fengling Li, Jingjing Li, Xin Li +1

    Representation-based safety defenses for vision-language models (VLMs) often learn to separate harmful from benign inputs, but separation alone does not establish what a risk score means or when it should be trusted. We introduce Counterfactual Neighborhood Risk Fields (CNRF), an evidence-grounded framework for selective VLM safety. CNRF constructs context-matched benign-harmful counterfactual pairs, uses their representation differences as capability-related risk directions, and estimates neighborhood support separately from risk. This separation prevents unfamiliarity from being conflated with harmfulness and enables selective intervention rather than blanket rejection. A victim-adaptive interface accommodates different safety observations across VLM architectures and routes selected requests for same-victim response review, which either releases acceptable candidates or produces constrained alternatives with reduced harmful capability. Across three VLMs and five multimodal jailbreak families, CNRF achieves 86.06-89.30% macro defense success rate while retaining substantial benign-task utility. Controlled experiments further show that query-matched references and preserved…

    AIMultimodal80PDF
  • arXiv stat.MLOct 14

    Posterior Frequencies and the Cost of Not Fitting: A Correction to a Memorization Lower Bound

    Zhenisbek Assylbekov, Rustem Takhanov

    Feldman's memorization lower bound assigns a frequency coefficient based on how often a point appears in the training sample. We give an exact three-point counterexample to its conditional and averaged forms, using independent fair labels and a permutation-equivariant learner. A sequence of sampling priors makes the ratio of the proposed penalty to actual excess risk grow exponentially, ruling out a universal positive constant-factor repair. The priors vary with sample size, the offending profiles become rare, and both quantities vanish. Retaining the original model, we obtain an exact replacement using full-sample posterior probabilities. Under exchangeability, these depend on multiplicity and the complete frequency profile. We characterize the largest multiplicity-only coefficients valid for unrestricted learners. The original coefficient has the posterior-mean form of Good's exact frequency formula and is an occupancy-weighted average of the corrected coefficients.

    AI10PDF
  • arXiv cs.LGAI safetyOct 14

    Training Language Models to Fail Gracefully

    Daman Arora, Xuecheng Liu, Himanshu Gaurav Singh, Fahim Tajwar, Abitha Thankaraj +4

    Reinforcement learning makes language models better at solving tasks, but the same mechanism also rewards guessing. When success is the only way to earn reward, opting for a safe fallback is always worse than a long-shot attempt. When a wrong action can corrupt a database or ship broken code, that incentive can be costly. Therefore, we propose training models to fail gracefully: to preserve successful behavior whenever possible while learning to execute a safe fallback policy when acting would be unreliable. However, naively using policy gradients with intermediate reward for fallback behavior is brittle due to unfavorable optimization dynamics. We address this challenge with GracefulRL, which reshapes the optimization landscape to promote graceful failure. Importantly, this behavior emerges directly from outcome-level reward signals, without annotations specifying when to fall back or additional inference-time machinery such as a verifier. Across a range of tasks and scales, we train models that largely preserve their task-solving capability while selectively falling back when acting would be unreliable. More broadly, our results show that reinforcement learning can not only…

    AITraining & fine-tuning80PDF
  • arXiv cs.SDVoice AIOct 14

    HRTF Upsampling and Personalization via Neural Operators

    Meenakshi Krishnan, You Zhang, Ramani Duraiswami, C. Phillip Brown

    Head-related transfer function (HRTF) spatial upsampling from sparse measurements is a fundamental challenge in personalized binaural audio. We formulate HRTF upsampling as an operator learning problem and introduce SPARO, a neural operator that maps sparse measurements at a set of spatial directions to the HRTF field, a continuous representation that can be queried at arbitrary positions. SPARO projects the measurements onto a learned set of spherical latent modes, processes the latent modes with Transformer blocks, and decodes the resulting representations at query directions. On SONICOM, using the LAP24 measurement configuration, SPARO attains the best log-spectral distortion (LSD) and interaural level difference (ILD) error at every sparsity level from 3 to 100 measurements (18% lower LSD and 43% lower ILD error than the strongest baseline at 100 measurements) while remaining competitive in interaural time difference (ITD) error. It reconstructs a dense HRTF 15 to 373 times faster than the leading neural-field method RANF. We further validate the approach on HUTUBS, demonstrating its effectiveness across HRTF datasets.

    AI60PDF
  • arXiv cs.CLTraining & fine-tuningOct 14▲ 67

    Cortex: Consolidating Context into Parametric Memory

    Jintian Zhang, Feng He, Xinnian Liang, Ziwen Xu, Haoming Xu +3

    Context can improve large language model performance, but these gains may be lost after context removal. Inspired by biological systems consolidation, we formulate Memory Consolidation (MC) as internalizing these gains while preserving non-target capabilities, connecting context-specified editing, skill learning, and behavioral steering under a common objective. We introduce a five-task-family testbed measuring consolidation and retention under single, batch, and sequential updates. We propose Cortex, which generates prospective queries to anticipate uses of context and distills context-conditioned outputs into mergeable low-rank or structural feed-forward network updates. Across 4B--120B dense and mixture-of-experts backbones, including both text-only and multimodal models, Cortex lies on the empirical consolidation--retention Pareto frontier in most evaluated settings. After 50 sequential factual edits on a 14B model, Cortex-Structural recovers 88.9% of the context-induced gain while retaining 99.8% of original non-target performance.

    AIFrontier LLMs90PDF
  • arXiv cs.CVComputer visionOct 14

    Where Should the Gaussians Go? Reusable Label-Aware Allocation for Budget-Constrained Gaussian Splatting of Cinematic CT

    Bowen Xiang, Dingjie Su, Jon S. Heiselman, Michael I. Miga

    Anatomical labels help 3D Gaussian splatting preserve vessels in cinematic renderings of CT, but the representation is still found by optimising each patient's scene, where densification decides where Gaussians go, while real-time displays cap their number. We address one part of this process: deciding where a fixed budget of B Gaussians should be placed. We propose a reusable, labelaware allocation front end that maps a patient's CT, labels and transfer function to a density over 2mm cells and draws exactly B positions from it without per-patient optimisation; the other Gaussian attributes are still fitted per patient. The front end is learned once from per-patient optimised scenes and implemented as a population-trained neural prior and an interpretable seven-parameter visibility-aware closed form (VACF). With identical attribute fitting on 20 held-out patients at 10K-100K Gaussians, both raise vessel-masked PSNR over uniform allocation by 4.5-7.1dB and over pruning a dense voxel-aligned model by 1.6-3.3dB. The neural prior does not differ significantly from per-patient optimised allocation at any budget, and VACF is within 0.6dB of it on vessels; on 19 external patients the…

    AI40PDF
  • arXiv cs.LGOct 14

    Spectral Indistinguishability and Learning-Curve Identifiability in Quadratic Networks

    Dekun Yang

    Motivated by information-organization sculpting, we study spectral summaries and scalar learning curves in centered quadratic networks. We construct analytic positive input densities with Gaussian one- and two-coordinate marginals. Their complete fourth-moment operators are orthogonally intertwined by a map fixing the entire diagonal teacher algebra, giving equal anchored spectral measures. For selected common teachers with a nonzero cycle invariant, their factorized loss curves differ because the map does not preserve matrix multiplication. Cycle length $r$, which can be arbitrarily large, gives $O(\varepsilon^r)$ contrast uniformly in training time for each fixed construction. For a supplied pair, noisy scalar loss queries require order $\varepsilon^{-2r}$ observations, while bounded initialization interventions with scalar gradient readouts require order $\varepsilon^{-2}$. A shared-input score, equal to a paired-gradient difference, attains the optimal input-sample exponent without knowing the teacher. An explicit family of diverging auxiliary teacher scales instead admits a nonzero finite limiting contrast at coupling $1/\log L$. Conversely, for Gaussian inputs and a…

    AI10PDF
  • arXiv cs.LGOct 14

    From Human Corrections to Learned Segmentations: A Critical Mathematical Review of Information, Learning, and Human Effort

    Jon Sporring

    When does a human correction become useful learning, and what establishes better segmentation at matched human effort? This critical narrative review follows feedback through its selection, learner update, and evaluation. It compares active learning, machine teaching, interactive segmentation, and sequential predictive evaluation using a common mathematical account of target information, predictive error, response predictability, and human work. An exact Bayesian update converts evaluation-label information into expected log-loss improvement; an excess-risk decomposition explains what an approximate learner can lose. Conditional information decompositions separate the roles of selected locations, labels, and displayed context. Empirical comparisons show why response protocols, update mechanisms, and effort denominators matter, distinguishing current-mask refinement from reusable learning and simulated interaction from human evidence. We identify what logs can reveal, what requires independent evaluation, and what remains model-dependent. The resulting research priorities are to test what selection reveals, whether using it improves assimilation, and whether the resulting…

    AI20PDF
  • arXiv cs.SEOct 14

    What Should a Software Team Spend on Agents? Simulation-Based Cost Optimization of Mixed AI-Human Teams

    Steffen Herbold

    Context: AI agents are rapidly adopted in software development, but their token costs are rising and it is unclear how much a team should spend on agents, and on which tasks. Objective: We determine how teams should divide spending between human developers and AI agents to deliver the features a project demands at the lowest total cost. Method: We model mixed teams of human developers and AI agents with eight specialized roles as a discrete-time stochastic process covering bug injection and detection, technical debt, human supervision of agents, and release cycles with feature freezes. Using Monte Carlo simulation and evolutionary search, we determine cost-optimal teams for 432 scenarios varying developer capability, project velocity, debt dynamics, agent capability and price, and bug costs, and compare them with a human-only baseline. Results: Escaped bugs are the dominant cost driver, and 61% of the agent budget goes to finding bugs. Agent capability matters more than token price, since higher-quality outputs reduce downstream quality assurance costs. In teams with capable agents, human developers mostly fix bugs and leave the implementation of new features to the agents. Teams…

    PDF
  • arXiv cs.CLInference & servingOct 14

    FOLD: Position-Free Far Memory with Local Order for Long-Context Speculative Decoding

    Shuxuan Li, Jialiang Kang, Yanghao Zhou, Ziye Zhu, Xiao Yang

    Speculative decoding accelerates LLM generation through lightweight drafting and parallel verification. It is particularly promising for long inputs, where KV-cache access costs can be amortized over multiple accepted tokens. Yet most drafters are trained on short sequences and degrade on long inputs, while long-context training is costly. Existing training-free approaches restrict the context window, rescale positions, or augment recent context with retrieved distant entries. Drafters, however, read target hidden states that already encode contextual and positional information, and their lightweight architecture makes full-history attention affordable. We propose FOLD, a training-free method that preserves exact local positions and treats all distant entries as far memory at a shared position. To counter attention dilution as the memory grows, we sharpen attention within it to emphasize relevant entries while separately controlling its total weight relative to the local window. Across six target-drafter pairs and diverse benchmarks of up to 256K tokens, FOLD predominantly delivers the highest speedup, with the best case more than doubling the speedup over autoregressive…

    AI80PDF
  • arXiv cs.CVAgentsOct 14

    STP-GUI: Spatially-Aware Token Pruning for Efficient Web-GUI Agents

    Shuo Wang, Sifan Zhou, Xiaobo Wang, Tianyu Fu, Chenxu Zhao +4

    With the rapid development of vision-language models (VLMs), Web-GUI agents have achieved promising performance in interacting with real-world interfaces through screenshot-based grounding. However, high-resolution screenshots and multi-turn histories introduce excessive visual tokens, causing substantial inference overhead. Existing token pruning methods, mainly designed for natural images, often select tokens independently based on importance scores, which can fragment the local visual structures required for recognizing small GUI elements such as buttons, icons, and hyperlinks. In this work, we propose STP-GUI (Spatially-aware Token Pruning for GUI agents), a query-guided token pruning framework that preserves fine-grained instruction-conditioned token scoring while performing spatially coherent window-level selection under aggressive token reduction. A lightweight selector is optimized through policy learning while the VLM backbone remains frozen. Experiments on Online-Mind2Web show that \papername achieves a favorable efficiency-performance tradeoff under low token budgets. At 21% token retention, it preserves 77.7% of full-token performance while reducing inference latency…

    AIMultimodalInference & serving80
  • arXiv cs.AIAgentsOct 14

    From Relevance to Validity: Zero-Shot Tool Selection via Functional and Contextual Validation

    Xiaoxuan Dong, Jin Zhang, Zichen Yuan, Yue Zhou, Zihao Rao +9

    Large language model (LLM) agents increasingly face tasks from unseen domains and execution contexts, making zero-shot tool selection essential for identifying tools that can satisfy task requirements. Existing dominant methods mainly rely on relevance ranking, but semantic matching alone cannot ensure that a tool is functionally compatible or remains applicable in a new context. We preliminarily identify a \emph{transfer-validity gap}, where semantically relevant tools may still fail to satisfy functional or contextual requirements after transfer. To address this gap, we propose \methodname{}, a progressive validation framework that complements semantic relevance with endpoint-grounded functional compatibility and task-conditioned contextual evidence. Across cross-domain and cross-dataset transfer, \methodname{} consistently improves capability ranking, with larger gains under functional or contextual ambiguity. Ablation results further confirm the complementary contributions of functional and contextual validation.

    AI70PDF
  • arXiv cs.CVAgentsOct 14

    Look Before You Restore: Learning to Predict Before Acting for Agentic Image Restoration

    Xu Zhang, Jun Wan, Lefei Zhang, Liangpei Zhang

    Agents for mixed-degradation image restoration typically learn what a tool does to an image only after running it: failed calls waste computation and harmful outputs go unnoticed until too late. We propose PreActIR, an agentic restoration framework that predicts tool consequences before execution. For each candidate tool, a tool-conditioned world model forecasts changes in residual degradation, image quality, and fidelity, together with the risk of harm and the uncertainty of the prediction. The agent executes the top-ranked candidate, and an independent verifier commits or rolls back its output before the agent replans from the observed state. PreActIR also audits whether performance is limited by the selector or by the candidate space, and studies targeted tool adaptation as a controlled way to expand that space. In a controlled evaluation that fixes the candidate pool, verifier, and budget and employs reference-annotated degradation states, PreActIR uses 42.5% fewer real tool calls and 63.4% fewer rejected calls than reactive, execute-then-evaluate trial and error, while improving Y-PSNR by 0.92 dB.

    AIComputer vision70PDF
  • arXiv cs.DSOct 14

    Hypothesis Selection via Weighted Medians

    Liang-Yu Zou

    Given $n$ distributions (hypotheses) $\mathcal H=\{H_1,\ldots,H_n\}$ and samples from an unknown target distribution $P$, proper hypothesis selection seeks a distribution whose total variation distance to $P$ is close to the best achievable within the known distributions. We give an algorithm that, using $O(\varepsilon^{-2}\log(n/δ))$ target samples, running in $O(n\varepsilon^{-2}\log(n/δ))$ time, with probability at least $1-δ$, returns $H_{\hat i}$ satisfying $d_\mathrm{TV}(P,H_{\hat i})\leq 3\min_{i\in[n]}d_\mathrm{TV}(P,H_i)+\varepsilon.$ The algorithm achieves the optimal approximation factor and sample complexity. The running time improves the dependence on the failure probability from polynomial to logarithmic in the state-of-the-art bound $\tilde{O}(n\varepsilon^{-2}δ^{-1})$ of Aamand, Aliakbarpour, Chen, and Silwal (COLT 2026), thereby answering their Question~2.

    AI10PDF
  • arXiv cs.CLEvalsOct 14

    From Triage to Impact: A Multi-Hop Reasoning Benchmark for Explainable Persian Hate Speech

    Ghazal Kalhor, Sima Kamandloo, Mehrsa Shamsian, Behnam Bahrak

    Hate speech has become increasingly prevalent on social media, raising concerns about its detection, interpretation, and moderation. Persian, a relatively low-resource language, lacks sufficient annotated data and computational resources for fine-grained hate speech analysis. In this study, we introduce a fine-grained, multi-hop benchmark for explainable Persian hate speech analysis, covering content triage, target identification, hate tactic and moral foundation analysis, textual evidence extraction, harmful narrative inference, and severity assessment. By evaluating state-of-the-art large language models (LLMs), we find that performance generally declines as tasks require more fine-grained semantic and inferential reasoning. Proprietary models generally outperform open-weight models, while reasoning-oriented models show no consistent advantage. Incorporating intermediate reasoning steps substantially improves severity prediction compared with one-shot assessment. Our analysis further reveals that Incitement to Violence or Harm is most strongly associated with higher severity, while political-affiliation targets are particularly associated with subversion-based moral framing.…

    AI50PDF
  • arXiv cs.LGOct 14

    ReAl: Refine neural operators via aligning dynamical flow

    Jing Xu, Zenglin Xu, Li Yi, Ruoxi Jiang, Yuan Qi

    Neural partial differential equation (PDE) surrogates trained with mean squared error (MSE) can capture dominant dynamics but often lose high-frequency information and fine-scale structures. Generative models (e.g., Flow Matching) can capture the distribution of fine-scale details, yet stochastic errors may accumulate during autoregressive rollouts and compromise long-term stability. To address these limitations, we introduce Refine and Align(ReAl), a model-agnostic plug-in that augments pretrained PDE surrogates to improve the reconstruction of high-frequency structures associated with complex physical phenomena. In the refinement stage, a conditional flow-matching model learns the residual distribution around the deterministic base prediction, recovering missing fine-scale structures while retaining the dominant dynamics. In the alignment stage, to mitigate the generative discrepancies amplified in the autoregressive rollout, particularly in chaotic systems, we generate multiple trajectories from the same input and reinforce the refiner with trajectory-level rewards, thereby achieving long-horizon dynamical alignment. Experiments across diverse surrogate architectures and PDE…

    AI30PDF
  • 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
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