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ByteTrail · AI and developer-tools intelligence
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All fields3362Multimodal537Agents507Frontier LLMs431Inference & serving260Image segmentation1783D scene158Learning verifiable rewards156Graph learning152Voice AI98Evals87Classifier & causal discovery86Forecasting time78Flow matching72Training & fine-tuning55Teacher policy distillation51All 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 LLMs11+1000.0%
  • AI safety41+583.3%
  • Image & video generation34+580.0%
  • Training & fine-tuning55+511.1%
  • Robotics27+440.0%
  • Computer vision43+437.5%
  • Evals87+314.3%
  • RAG11+175.0%

Papers

  • arXiv cs.SDOct 18

    DF-PALM: Dynamic Fusion Prompt Learning for Audio--Language Models

    Yulin Sun, Shenghua Li, Yong Dou, Xinwang Liu, Cheng Yang +1

    Few-shot audio classification requires audio--language models to distinguish variable acoustic events from limited labeled data. PALM's main formulation adapts encoded class representations without changing pretrained encoders, but its class representations remain input-independent and classification uses audio features without class-conditioned refinement. We introduce DF-PALM, a dynamic feature-space prompting framework that combines audio-conditioned class representations with asymmetric cross-attention for audio refinement. Class representations are conditioned after text encoding, held fixed across fusion layers for each input, and reused for final scoring. Both pretrained encoders remain frozen, avoiding audio-conditioned text-encoder passes. With PENGI as the frozen backbone, the main 16-shot evaluation covered 11 benchmarks and six baselines. DF-PALM achieved 80.69% mean accuracy and 80.32% macro-averaged F1, exceeding PALM by 4.11 and 4.30 percentage points, respectively. Paired benchmark-level tests supported both gains after Holm correction. Across four shot settings, DF-PALM achieved the highest mean accuracy and macro-averaged F1 among the four evaluated…

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  • arXiv cs.LGOct 17

    CARTA: Self-Supervised Reusable Transaction Embeddings via Heterogeneous Tokenization and Cross-Attention

    Shreyas K. Chandrahas, Jose Munoz, Satish Babu Bellapu

    Acquiring fraud labels for payment transactions is expensive and inconsistent across tasks, and high-cardinality identifiers such as merchant names make one-hot feature pipelines infeasible at scale. We present CARTA, a self-supervised encoder that learns a compact 16-dim embedding from raw transactions without labels; the frozen embedding is then reused for label-efficient fraud probing and exploratory unsupervised risk stratification without retraining the encoder. CARTA uses type-specific projections with learned embedding tables for high-cardinality identifiers, trained with token excision and InfoNCE contrastive loss. On IEEE-CIS, its linear probe at 1% of labels exceeds a raw-feature probe at 100%; on IBM TabFormer (2M rows), its frozen embedding reaches GBDT AUPRC 0.508, above frequency encoding (0.411) and the merchant-dropping baseline (0.022), though fully-supervised CatBoost is stronger. CARTA is a label-free, reusable representation that complements task-specific supervised models.

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  • arXiv cs.CLOct 17

    How Well Can LLMs Verbalize Mathematical Formulas? A Multi-Domain Evaluation

    Peter Volbach, Tim Schlippe

    Large Language Models (LLMs) have shown strong capabilities across natural language tasks. But their ability to generate accurate written verbalizations of mathematical formulas has not been systematically evaluated. Such verbalization is important for text-to-speech systems supporting audio-based learning and visually impaired users. We evaluated five state-of-the-art LLMs across six scientific domains and constructed Read-Aloud Math, a balanced multi-domain corpus with LaTeX formulas, manually validated simple and extended transcriptions, multiple variants, textual context, domain labels, and formula complexity levels. Using multi-reference BLEU, GPT-5.2 achieved the highest score for simple transcriptions (75.9% without context, 64.9% with context), although without context it did not significantly outperform Grok 4.1 Fast or Claude Sonnet 4.5. Claude Sonnet 4.5 achieved the highest score for extended transcriptions without context (36.5%), and Grok 4.1 Fast with context (48.8%). For extended transcriptions, Statistics and Mathematics scored highest across the two context settings, while Physics scored lowest. Formula length was significantly negatively associated with BLEU…

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  • arXiv cs.CROct 17

    Same Data, Different Bytes: Serialization-Invariant Integrity Checks for Agent-Published Datasets

    Jin Gao, Maura Clark

    Agent networks require reliable ways for requesting agents to verify where data is located, how it can be accessed, and whether the data received matches what the data owner made available. Checksum mismatches can reflect either altered content or serialization differences that preserve data equivalence under specified rules. Data identification mechanisms for agents, such as DataFacts, describe where data can be accessed and provide checksums for checking received data against published references. We propose a serialization-invariant identification method that assigns the same identifier to data representations equivalent under explicit rules. We use DataFacts as a case study, retaining its existing checksum alongside the proposed identifier. In a controlled agent study, we compare the proposed configuration with ordinary-tool and checksum baselines across equivalent re-encodings, altered content, and unavailable references. The study includes three model tiers from each of two providers. For equivalent re-encodings, the proposed configuration increased expected-outcome rates by 80.4 percentage points over the ordinary-tool baseline and by 70.2 percentage points over the…

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  • arXiv cs.LGOct 17

    Arctic-SWE: Revisiting the Long Horizon Agent Training by Modeling-System Co-Design

    Boyi Liu, Yite Wang, Xinyu Lian, Fan Shu, Sida Li +4

    Long-horizon agent training, including software engineering, is advancing rapidly, but the recipe and infrastructure needed for a strong reinforcement-learning baseline remain difficult to reproduce. This makes gains from new algorithms or data hard to distinguish from improvements to an under-tuned baseline. We present Arctic-SWE, a fully asynchronous RL framework that establishes such a baseline through model-system co-design, using overfitting on a small task pool as the testbed. This testbed exposes failures that early reward gains conceal and guides the integration of established components: gated reward, importance-weight control, aligned training and inference engines, and consistent state across policy updates. No single component is new, yet together they form the missing foundation. On raw R2E-Gym data, without new algorithms or data work, Arctic-SWE trains Qwen3.5-4B to 62.0% pass@1 on SWE-bench Verified. This is comparable to concurrent FrogNano (61.5%), which relies on online task synthesis, after about one-sixth as many training rollouts. Building on this foundation, we further filter and refine R2E-Gym, SWE-rebench V2, and Scale-SWE. With this curated subset, the…

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  • arXiv cs.IROct 17

    GRaSP: Latency-Aware Optimization for Geo-Distributed Retrieval-Augmented Generation

    Hardik Ruparel, Raj Kumar Pandey, Pramod Kumar

    Retrieval-Augmented Generation (RAG) is increasingly served across geo-distributed cloud and edge infrastructures, where wide-area latency, cross-region egress cost, and data-residency rules compete with retrieval quality. Conventional pipelines rank by semantic similarity alone and ignore where content lives, inflating tail latency and cost. We formalize the Geo-RAG Selection Problem (GRaSP), a drop-in re-scoring layer that decays relevance by latency and adds an optional cost penalty under a cardinality budget and per-region caps, with no retraining. We first settle its complexity: the base formulation is a linear objective over a partition matroid, solved optimally by an O(N log N) greedy algorithm, rather than NP-hard as a knapsack framing suggests, with hardness entering only under an additive egress budget. On a five-node CloudLab testbed emulating a four-region WAN over the Natural Questions corpus (2.68M passages), GRaSP's benefit is placement-dependent: under uniform placement it cuts fetch latency at negligible quality cost; under topical (residency-like) placement it traces a recall-latency Pareto frontier that dominates relevance-only, nearest-k, and a…

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  • arXiv cs.AIOct 17

    A Cross-Domain Evaluation of Prompting Techniques for Large Language Models

    Kevin Krazius, Tim Schlippe, Paul Libbrecht

    Prompt engineering has become a central mechanism for guiding the behavior of Large Language Models (LLMs) across a wide range of reasoning and language understanding tasks [1], [2]. However, existing studies frequently evaluate prompting techniques using different LLMs, datasets, or task settings, making direct comparisons across methods and domains difficult [3]. Consequently, in this paper we present a controlled cross-domain evaluation of Zero-Shot prompting, Few-Shot prompting, Retrieval-Augmented Generation (RAG), and automatic prompt optimization based on a PE2-inspired optimization pipeline [4]-[6]. All experiments were conducted with the same LLM and evaluated on six benchmark datasets covering arithmetic reasoning, logical reasoning, commonsense reasoning, and factual verification. The results show that no prompting strategy consistently outperforms the others across all domains, and pairwise statistical comparisons of the highest-performing configurations revealed no significant differences. RAG achieves the highest overall average accuracy of 78.33% when 300 examples are available for retrieval, while Few-Shot Prompting achieves comparatively high accuracies across…

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  • arXiv eess.SYOct 17

    ATLAS: Expert-Guided Sustainable Satellite Backhaul for Off-Grid Wildlife Monitoring

    Hao Fang, Miao Zhang, Haoyuan Zhao, Long Chen, Yi Ching Chou +3

    Remote wildlife monitoring increasingly depends on high-fidelity cameras and sensors deployed far from grid power and terrestrial networks. Low Earth orbit satellite links can provide connectivity in these settings, but their high power draw and variable bandwidth, combined with intermittent on-site solar harvesting, make continuous backhaul infeasible. Current systems therefore filter at the edge, with motion triggers that waste limited energy and bandwidth on environmental artifacts, or fixed vision models that cannot follow evolving expert objectives. We present \textsc{ATLAS}, an expert-guided edge-to-cloud system that treats satellite backhaul as a scarce resource for off-grid wildlife monitoring. \textsc{ATLAS} uses foundation models to compile natural-language expert intent into semantic scoring logic, combines semantic relevance with environmental and energy state, and uses a constrained reinforcement-learning scheduler to decide which clips to upload and when. As a representative case study, we deployed \textsc{ATLAS} for over six months across 18 off-grid salmon-monitoring river sites. \textsc{ATLAS} achieves a mean clip-selection accuracy of $87.4\%$, exceeding mean…

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  • arXiv cs.LGOct 17

    A Symmetry-Matching Approach to Blind-Spot Reduction in Sparse Autoencoders

    Csaba Balogh

    Language models can treat semantically distinct inputs as interchangeable at the representation level, creating blind spots that standard sparse autoencoder (SAE) objectives do not target. In safety-critical settings such as clinical dosage extraction, legal clause interpretation, or financial amount verification, such failures can propagate silently into downstream decisions. We study this problem as one of feature-basis orientation. Building on a symmetry-matching view from algebraic error-detection theory, we add a differentiable $V_{\mathrm{Gini}}$ regularisation term to SAE training that penalises uneven perturbation response within a specified family. Across GPT-2, Gemma 2, and Qwen 2.5, a frozen multi-seed evaluation shows that $V$-regularisation raises both relative and absolute lower-tail responses while reducing response-profile imbalance, with no material general-text reconstruction degradation. JumpReLU and minimum description length (MDL) objectives remain close to the Standard SAE profile under the same protocol. Family-geometry controls show that the effect is not explained by template duplication or a single dominant perturbation family, and held-out probes plus…

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  • arXiv cs.CROct 17

    CAT-RAG: Improving Secure Code Generation with Category-Aware Agentic Retrieval

    Mahdieh Abazar, Lorenzo De Carli, Benjamin Tan

    Despite rapid progress in LLM-based code generation, security vulnerabilities remain widespread, and retrieval-augmented code generation (RACG) can further amplify risk when retrieved knowledge is verbose or vulnerable to poisoning. Existing secure RACG approaches inject security knowledge at a fine-grained vulnerability level, causing prompt overhead and diluted model attention when a query implicates multiple related weaknesses. We propose CAT-RAG, an agentic security category-aware retrieval-augmented code generation framework. CAT-RAG performs adaptive category selection via an LLM as a categorizer agent and operates either as a standalone security layer or as a drop-in add-on to RACG pipelines, requiring no modification to the underlying model. We replace CWE-level retrieval with compact, language-specific security categories. These categories provide a higher-level abstraction over individual CWE weaknesses, organizing them by the security concern they share, such as Input Validation and Injection Attacks, rather than merely grouping related entries. Each category consolidates best practices and secure code examples into a single, prompt-efficient representation. We perform…

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  • arXiv cs.CLOct 17

    Compositional Activation Steering Across Languages and Behaviors with Contextual Gates

    Daniil Gurgurov, Akansh Maurya, Josef van Genabith, Simon Ostermann, Hitomi Yanaka

    Activation steering has emerged as an effective and precise way to influence the behaviours of language models. However, composing multiple steering directions to control several behaviours simultaneously remains challenging, particularly in multilingual settings and with training-free steering vectors. We address this challenge with contextual steering, jointly training gates that adapt the strength of fixed steering directions to the current hidden state and interactions between interventions. Across a broad range of languages, behavioural attributes, and models, our approach outperforms constant-strength steering and four strong baselines, including prompting. It also better preserves general knowledge and keeps the model's internal computation closer to that of the prompted model. Our analysis further shows how jointly trained gates adapt their contributions during composition, explaining the mechanisms underlying effective compositional steering.

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  • arXiv cs.HCOct 17

    Leveraging Mental Models for Human-Centered Explanations: A Systematic Design Approach Illustrated by Explainable Fake Review Identification

    Sidra Naveed, Md Shajalal, Gunnar Stevens, Alexander Boden

    Explainable AI (XAI) often prioritizes model fidelity over user intelligibility, yielding explanations that satisfy technical transparency yet fail to support human sense-making. We propose a human-centered approach, MIND-X (Mental-model INtegrated Design for eXplainability), which aligns explanations with users' mental models, commonsense concepts, and judgment heuristics. Using fake review detection as a case study, we contribute: (1) a qualitative study (n=10) that elicits how people assess review credibility and produces an actionable, design-guiding taxonomy of perceptual cues; (2) a set of design principles that link those cues to explanation strategies and prompt LLMs to generate adaptive, language-based explanations; and (3) a quantitative evaluation (n=166) showing that these explanations offer distinct advantages over token-level transparency methods widely used in explainable text classification in terms of understandability, satisfaction, perceived trust, persuasiveness, and decision-making support. Rather than claiming general superiority, our evaluation illustrates how grounding explanations in users' everyday reasoning practices can produce more intelligible and…

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  • arXiv cs.SEOct 17

    RXSynth: A Seed-Grounded Corpus for Studying Java Runtime-Exception Bugs

    Parnian Eslamloo, Banani Roy

    Existing Java bug benchmarks offer limited method-level data for studying runtime-exception detection. Filtering Defects4J, Bugs.jar, Bears, and BugSwarm yields only 170 real buggy/fixed method pairs across seven unchecked exception types. We introduce RXSynth, a corpus of 4,275 synthetic Java method pairs generated from real bug/fix exemplars. We select the generator using structural alignment with real seeds and validate pairs using a two-stage patch-anchored procedure that identifies the labelled exception risk and checks whether the repair removes it. Independent JVM execution confirms patch-local validity for 84.2% of a stratified sample of 500 pairs. Provenance records enable evaluation without overlap between generation exemplars and test bugs. Structural analyses show good alignment within the represented size range, but compression of larger methods. We use RXSynth to compare isolated-method detection with pair discrimination when the repair is visible. Under provenance-controlled evaluation, supervised isolated-method ROC-AUC is about 0.55-0.64. Stronger zero-shot models reach about 69% accuracy but identify only 41.2% of repaired methods correctly. In contrast,…

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  • arXiv cs.AIOct 17▲ 6

    HybridResearcher: Bridging the Structured-Unstructured Boundary for Deep Research Agents

    Soyoung Yoon, Peiran Xu, Han Wang, Ruofan Wu, Xiaodong Yu +6

    While LLM agents are increasingly capable of gathering and synthesizing evidence from a single homogeneous source, such as structured tables or unstructured data, real-world analytical questions frequently span heterogeneous sources. For example, answering one enterprise question may require both a result computed over an internal database and evidence found on the web. Recent evaluations on benchmarks like HybridDeepResearch show that even frontier agents struggle when evidence must cross this boundary. While these benchmarks have exposed the limitation, methodologies to explicitly teach cross-source reasoning remain absent. To address this, we introduce HybridResearcher, a 4B agent trained to bridge evidence across the structured and unstructured boundary. To build it, we synthesize cross-source bridge trajectories by starting with validated single-source tasks-such as existing text-to-SQL or web search queries-and fabricating the missing source around a bridge entity drawn from the seed's own output. We retain a sample only when strict construction gates confirm that neither side alone suffices, ensuring the data requires true cross-source interdependence. Finally, we train on…

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  • arXiv cs.CVOct 17

    Predicting Individual Differences in Lipreading From Eye-Tracking and Pupillometry During Silent Visual Speech

    Puneet Kumar, Satyarth Malik, Gao Zheng, Guoying Zhao, Xiaobai Li

    Lipreading ability varies widely, including among people who are hard of hearing. Although mouth-directed attention is linked to speechreading outcomes, it remains unclear how much lipreading skill can be inferred from viewing behavior and which signals carry this information. We examine whether individual differences in lipreading ability are reflected in viewing behavior during silent speech and quantify the contributions of gaze dynamics, pupil-linked signals, and spatial viewing allocation. We introduce the Lipreading Perception Study (LiPS) dataset and a tracker-independent preprocessing pipeline that synchronizes SR Research EyeLink gaze and pupil recordings with the stimuli. Twenty-four adults (age 32 $\pm$ 10 years) completed a four-alternative forced-choice silent lipreading task. An extreme-groups protocol yielded 18 participants labeled as stronger or weaker lipreaders. Each participant-trial included four aligned views: lip-region images, frame-level gaze, pupil summaries, and spatial viewing proportions. A four-branch neural network with cross-modal attention predicted labels using participant-disjoint splits and systematic ablations. The full model reached accuracy…

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  • arXiv cs.LGOct 17

    Distribution-Based Regularization for Reward Fine-Tuning Diffusion and Flow Models

    Romeo Passaro, Jeongjae Lee, Jong Chul Ye, Michael M. Bronstein, Yoshua Bengio +2

    Reward fine-tuning is an effective way to adapt pretrained diffusion and flow-matching models to downstream objectives, but aggressive optimization often leads to mode collapse and distributional drift. Existing methods attempt to control this drift with reverse-KL regularization, implemented as an $\ell_2$ penalty on the velocity field; however, this functions by indirectly constraining sampling dynamics rather than directly acting on the generated distribution. To address this, we propose DFR (Distributional Functional Rewards), which augments the task reward with a distribution-dependent, per-sample reward that acts explicitly on the generated distribution. Formally, we generalize the KL-regularized objective from expected rewards, which are linear in the model's distribution, to convex functionals of it. The first variation of this objective defines an effective, distribution-dependent reward that directly penalizes deviation from the pretrained distribution. This formulation yields additive per-sample rewards for the Fréchet distance (FD), maximum mean discrepancy (MMD), and DreamSim diversity, recovering several recently proposed rewards as special cases. Across various…

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  • arXiv cs.CROct 17

    Peer Pressure: Execution-Verified Co-Evolution of Attacker and Defender Populations for Code Security

    Khushbu Pahwa, Yubin Ge, Daniele Bonadiman, Xiaofei Ma, Yi Zhang

    LLM code agents increasingly ship code with little oversight, yet defenders trained on static vulnerability datasets fall behind adapting adversaries. We co-train attacker and defender instead, and let execution decide every reward: an attacker (Red) edits a real repository and earns nothing unless its patch applies, builds, passes the tests, completes its development task and reproduces a real exploit in an isolated container, whose path also labels the entry point, the critical operation and the trace between them. Peer-Relative Policy Optimization (PRPO) makes both sides populations of LoRA adapters over one frozen base and credits each against same-role peers that faced an identical condition, placing the population inside the policy-gradient baseline rather than the opponent pool, with a tournament preserving diversity. Matching cancels the condition's difficulty: at equal coverage it is worth 2.3 points of balanced accuracy over a baseline computed on other conditions, and it matches a within-policy control at half the verifier spend. PRPO's 7B diff reviewer reaches 64.8% balanced accuracy on four unseen applications in three untrained languages, 5.5 points above supervised…

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  • arXiv cs.CLOct 17

    LastWords: Boring Problems Revisited

    Huayang Li, Richard Sproat

    In 2022, the second author published a Last Words piece in Computational Linguistics in which he pointed out that, despite the advances in AI up to that point, there were still some problems in natural language processing that on the one hand seemed trivial and perhaps boring, but on the other were hard to solve using purely neural methods. One such problem discussed in that context was text normalization, and a particular subproblem of that, namely number name reading. The piece came out just a few months before the first release of ChatGPT, and needless to say, a lot has happened since then. Indeed, as we document here, the problem (of number name reading) has practically been solved, at least by one of the latest frontier models. However, we believe that there are still open issues for such limited domain, constrained and limited resource problems, and that in some future machine learning approach, the massive resources of the latest frontier models would not be needed to solve them.

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  • arXiv cs.CVOct 17

    Multi-Speed Field Evaluation of a Semi-Autonomous Precision Sprayer for Weed Control in Vegetable Crops

    Md Khairul Islam, Boyang Deng, Yuzhen Lu, Daniel Brainard, Ajit Srivastava

    Machine vision-guided precision spraying is promising for sustainable vegetable production by enabling site-specific weed targeting, reducing herbicide application while protecting crop yield and quality. However, the effects of operating speed of sprayer systems on the integrated detection-tracking-actuation pipeline remain insufficiently characterized under field conditions. This study evaluated a semi-autonomous, tractor-based spraying system for targeted weed control in radish and quantified performance across three operating speeds. A five-season dataset comprising 10,599 images and 84,311 annotated instances across seven classes was curated for model development. A YOLOv11-medium model was integrated with ByteTrack tracking and a customized 12-nozzle sprayer on a semi-autonomous electric tractor. Field trials were conducted at 0.13, 0.27, and 0.45 m/s (i.e., 0.3, 0.6, and 1.0 mph) to characterize speed-dependent changes in weed detection, targeting, actuation, and crop damage. At the lowest speed of 0.13 m/s, the system achieved 94.6% mAP@50 and 93.2% recall for weed and crop detection. However, increasing platform speed substantially reduced system performance, with weed…

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  • arXiv stat.MLOct 17

    Scalable and Adaptive Distribution Regression via Random Features

    Yinsong Wang, Xiao Liu, Yu Ding

    Distribution regression seeks to learn mappings from probability distributions to response variables and has emerged as a unifying framework for kernel methods, multi-instance learning, and set-valued learning. Despite its success, kernel-based distribution regression remains computationally expensive and sensitive to kernel choice, motivating scalable and adaptive representations. Building on random feature approximations, we develop a finite-sample analysis of Random Feature Distribution Regression (RaDR) under two-stage sampling. Our perturbation analysis separates the effects of distribution-level sampling, finite within-bag sampling, and random feature approximation. We establish sufficient feature-budget conditions under which RaDR preserves the order of our excess-risk upper bound for exact two-stage kernel distribution regression while maintaining computational efficiency. We further propose Adaptive Random Feature Distribution Regression (A-RaDR), a unified score-guided framework that adaptively allocates random features according to downstream learning objectives. Experiments on synthetic benchmarks and 41 real-world datasets demonstrate that RaDR delivers substantial…

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