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ByteTrail · AI and developer-tools intelligence
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All fields3342Multimodal537Agents506Frontier LLMs431Inference & serving258Image segmentation1753D scene157Learning verifiable rewards153Graph learning152Voice AI98Classifier & causal discovery86Evals86Forecasting time78Flow matching71Training & fine-tuning53Teacher policy distillation50All 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%
  • Image & video generation34+580.0%
  • Robotics27+440.0%
  • AI safety39+387.5%
  • Training & fine-tuning53+381.8%
  • Computer vision41+310.0%
  • Evals86+290.9%
  • RAG11+175.0%

Papers

  • arXiv cs.CLOct 17

    GLOSS-MT: Controllable Low-Resource Machine Translation with an Editable Dictionary

    Mich-Seth Owusu, Stephen E. Moore, Akwasi Asare, Lawrence Adu Gyamfi, Paul Azunre +2

    Adapting a pretrained machine translation (MT) model to new terminology usually requires new parallel data and another round of fine-tuning, which is costly for low-resource languages. We ask whether controllability can be added to MT, and the fine-tuning burden reduced, without degrading the pretrained model. GLOSS-MT looks up the source's noun phrases in an offline English-Twi dictionary, replaces every phrase found by its Twi translation in place, and fine-tunes a translation model once to translate these mixed inputs. We apply the same data and pipeline to NLLB-200 (600M, 1.3B, 3.3B), OPUS-MT en-tw and TranslateGemma 4B. On a document-level English-to-Asante Twi benchmark (84 documents, four domains), every fine-tune scores above its pretrained base in chrF2 (+2.85 to +5.48), and the three NLLB fine-tunes are the best open systems (50.12-50.80). A direct controllability test on human-translated Bible passages shows that the three fine-tunes tested write 79-97% of the dictionary terms, and that replacing every dictionary entry by a different term changes the output accordingly (70-97% of the new terms appear) with no retraining. We release the models, data and code.

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  • arXiv cs.LGOct 17

    fxBench: Evaluating and Understanding Formula Suggestions in Spreadsheets

    Sanket Mhatre, Sumit Gulwani, Vu Le, Yasharth Bajpai, Gust Verbruggen

    Spreadsheets are among the most widely used tools for computations, and formulas are their core building blocks. Besides agentic spreadsheet tools, proactive formula suggestions enable significant productivity gains and are now available in popular spreadsheet software (Microsoft Excel and Google Sheets). Such suggestions pose interesting challenges: the right context must be collected and represented for a machine learning model, which must then be able to write a correct formula, in a way that is fast and cheap enough to trigger on every = (or subsequent keystrokes for auto-completion) without taking users out of their editing flow. Surprisingly, there is no public benchmark for formula suggestions on real spreadsheets. In this paper, we therefore (1) introduce fxBench as a manually curated benchmark of 503 formula suggestion tasks based on Sheetpedia, (2) break down the formula suggestion problem into four capabilities (gather context, understand context, understand intent, write formula) that a formula suggestion system requires, and (3) evaluate existing and new approaches to understand bottlenecks in these capabilities that pose interesting research directions.…

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  • arXiv eess.ASOct 17

    ShlokaDhara: A Multi-Speaker Sanskrit Speech Corpus for Learning Meter-Aware Prosody in Text-to-Recitation

    Harshal Kausadikar, Onkar Kishor Susladkar, Prathmesh Santosh Choudhari, Vedangi Deepak Deshpande, Ismini Lorentzu +1

    Sanskrit verse recitation is governed by $\textit{Chandas}$, a strict prosodic framework defined by rhythmic $\textit{Laghu}$ (light) and $\textit{Guru}$ (heavy) syllables. Because existing Sanskrit speech datasets prioritize ASR or conversational TTS, they lack the metrical fidelity required for authentic verse synthesis. We introduce $\textbf{\textit{ShlokaDhara}}$, a 43-hour, multi-speaker Sanskrit speech corpus capturing diverse meters, recitation styles, and melodic variations. We specifically focus on $\textit{Text-to-Recite}$, a synthesis task explicitly requiring both prosodic accuracy and metrical correctness. Benchmarking state-of-the-art TTS architectures via an automated UTMOS (neural MOS) predictor demonstrates that XTTS-v2 achieves the highest perceptual quality. We further validate the XTTS-v2 generated samples using two paradigms: subjective assessments by 16 Sanskrit experts and novel empirical metrics, specifically, syllable-level $\textit{Laghu-Guru}$ duration analysis to objectively quantify structural metrical awareness. Ultimately, this work establishes a robust dataset and evaluation benchmark for prosody-aware computational Sanskrit recitation. Generated…

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  • arXiv cs.AIOct 17

    Where Natural Language Ends in Agents: Executable Skill Composition with MetaSkill

    Meng Shu, Wei He, Hang Zhou, Runke Liu, Kai Han +1

    Agent Skills express open-ended task knowledge in natural language, but composing them requires persistent execution order and upstream-output handoffs. We propose selective formalization at Skill boundaries: coordination relations with stable references and machine-checkable constraints become executable, while substantive task judgments remain within Skills. We introduce MetaSkill, a hybrid representation that preserves complete Skill packages and local instructions while making invocation dependencies and predecessor-text bindings executable through a directed acyclic graph (DAG) runtime. This interface supports explicit coordination without prescribing how each Skill performs its local task. Across 20 multi-Skill artifact tasks and two LLMs, we compare natural-language procedures, prompt-visible graphs, and MetaSkill derived from a common frozen plan per task, using the same designated Skill packages. Additional comparisons evaluate AgentSkillOS with the same available Skill packages as MetaSkill, as well as agent systems using their configured tools. Blinded artifact judgments yield favorable preference point estimates across all reported comparisons, with 95% task-bootstrap…

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  • arXiv cs.CLOct 17

    PV-Decoding: Progressive Verification Decoding with Decode Gating for Diffusion Language Models

    Renxi Liu, Yiming Li, Shengyin Sun, Chen Chen, Xian Wang +6

    Discrete diffusion language models (dLLMs) offer an attractive alternative to autoregressive generation by updating multiple tokens in parallel, but their inference efficiency is still limited by the cost of safely committing tentative predictions. In particular, speculative decoding for dLLMs is challenging because speculative tokens within the same diffusion block may depend on one another, making aggressive acceptance prone to error propagation and strict verification computationally expensive. We present PV-Decoding, a confidence-driven speculative decoding framework with progressive verification for block diffusion inference. PV-Decoding partitions masked positions into directly decoded tokens, speculative tokens, and unresolved tokens using two confidence thresholds, and then verifies speculative tokens with progressively stronger policies, including single-batch, dual-batch, and multi-batch verification. This design exposes a practical accuracy--efficiency trade-off: weaker verification minimizes overhead, whereas stronger verification improves acceptance precision and robustness. Experiments on three diffusion language models across GSM8K, MBPP, and HumanEval show that…

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  • arXiv cs.LGOct 17

    Tensor Network Datamodels for Training Data Attribution

    Farzaneh Heidari, Md Rifat Arefin, Louis Jaburi, Guillaume Rabusseau

    Datamodels predict how a model's behavior changes when different parts of its training data are included or removed. However, linear datamodels assign fixed contributions to training units and cannot represent non-additive joint effects. We introduce Tensor Network Datamodels (TNDMs), which use low-rank tensor networks to model dependencies among training units while keeping the representation compact. TNDMs learn a map from subsets of the training data to the outcome of training on those subsets, allowing them to represent interactions among several training units. We prove that complementary and redundant training groups can induce high-degree subset functions with compact low-rank representations. A single fitted TNDM can then be used for several training data questions, including predicting the effect of removing data, measuring the importance of individual training units, identifying interactions between training units, and selecting useful subsets of the training data. We evaluate TNDMs on controlled tasks designed to contain strong interactions, as well as standard fine-tuning settings for classification and reasoning. On a controlled reasoning task, TNDMs outperform every…

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  • arXiv cs.AIOct 17

    Neuro-Inspired Inhibitory Message Passing for Accurate and Token-Efficient Multi-Agent Reasoning

    Nima H. Siboni, Magdalena Springer, Vahid Rostami

    Multi-agent systems promise to solve problems by bringing together information beyond the reach of any individual agent. Retrieval and tool use can distribute this information across agents, making communication essential to collaboration. A central challenge is keeping crucial evidence visible in each agent's context as messages accumulate. Repeated information can crowd out rare, decisive evidence. Models of cortical circuits suggest a principle for addressing this imbalance: inhibition can selectively suppress shared inputs while preserving sensitivity to weaker signals. We adapt this principle into inhibitory message passing, a training-free communication router that cancels redundant evidence separately for each evidence sentence and receiver. Each round, it forwards evidence sentences restated in agents' messages, delivering each at most once to receivers whose own evidence lacks it and excluding the senders' reasoning and answers. Against standard message passing, accuracy in peer groups rose by 39-77 percentage points across three benchmarks, and tokens per run fell by 10-46%. Gains held across seven open models spanning four to thirty-two billion parameters and two…

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  • arXiv cs.CLOct 17

    DiffuPlan: Editable Planning State for Diffusion Language Model Agents

    Qisheng Zhou, Zhen Xiong, Qiaoyu Tan

    Diffusion language models (dLLMs) are emerging as an alternative to autoregressive (AR) language models, yet dLLM agents largely inherit an interaction protocol designed for AR generation: once intermediate state is written, later observations can only be incorporated by appending more text. This is a poor match to a defining capability of dLLMs -- reconstructing earlier text conditioned on context that appears both before and after it. We ask whether this capability can support a different agent-state paradigm in which intermediate planning state is directly revisable as evidence arrives. We introduce DiffuPlan, a diffusion search agent that maintains a single editable planning state and reconstructs only the affected steps when later evidence resolves an entity or changes the search route. To support variable-length revisions, we introduce Expansion-Partition Marginalization (EPM), which marginalizes over equivalent expansion paths. We further adapt diffusion trajectory RL to multi-turn search. Across four multi-hop QA benchmarks, DiffuPlan achieves 50.24 average F1, outperforming strong AR search agents. More importantly, under identical data and training, editable planning…

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  • arXiv cs.CLOct 17

    When Do Repetition Loops Lock? Truncation Sampling, Bounded Penalties, and a Blind Spot in DRY

    Yichuan Chen, Xin Wang

    Language models sometimes repeat verbatim until the generation limit. We study the exit rather than the entrance: given that a span has already been repeated $n$ times, when is escape still possible? Along the loop the context is deterministic, so loop survival under any sampler can be computed exactly from one forward pass. Pure sampling leaves a positive escape probability at every step; top-$p$ with threshold $π>1/2$ locks a position exactly when the continuation probability reaches $π$. A bounded penalty can leave a position locked; a penalty that outgrows the model's preference for the loop over unseen tokens guarantees escape under top-$p$. The same analysis predicts a blind spot in the widely deployed DRY sampler: its sequence breakers cap the penalty at $λb^{g-1-a}$ for loops whose longest breaker-free run has $g$ tokens. On eleven open models, 66\% of locked units with $g=6$ stay locked under DRY and 1\% with $g=8$. In free generation with llama.cpp, all 109 loops that survive DRY have $g\le 7$. We propose DRY-C, which adds a term that grows exponentially with the number of verbatim recurrences, up to its look-back window. With it, none of 720 structured-output…

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  • arXiv cs.SEOct 17

    Vibenix: Agentic Software Packaging and Maintenance with Nix

    Martim Monis, Martin Schwaighofer, Nuno Saavedra, Alexandra Mendes, Rene Mayrhofer +1

    Implicit dependencies, uncontrolled network access, and other build-environment impurities create intransparent software supply chains. Identifying every dependency affecting a build is a difficult task. Functional package management addresses this through reproducible packaging, ensuring software relies exclusively on declared, hermetically isolated inputs. However, creating build recipes remains complex and time-consuming, posing a significant barrier to adoption for developers and maintainers. We present Vibenix, an open-source AI-powered agent that bridges this gap by automatically generating Nix packaging expressions. Vibenix uses a large language model (LLM) to iteratively refine recipes through a rule-guided build-repair feedback loop. On 466 packaging tasks from our new NixBench dataset, Vibenix produced buildable packages in 95.3% of cases using Claude Haiku 4.5, compared with 5.6% for a state-of-the-art LLM-based single-shot packaging tool. Manual validation of 100 packages revealed that 47% of the buildable packages were initially functionally correct; a post-build refinement process based on the evaluator-optimizer pattern increased functional correctness by 35…

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  • arXiv q-bio.NCOct 17

    Multilingual Brain Decoding Reveals Shared Cortical Semantic Geometry

    Yi Guo, Yong Hu, Michael Kwok-Po Ng, Shuqiang Wang

    Human languages differ markedly in lexical and syntactic form yet can convey similar semantic content. The neural basis of this cross-language semantic commonality remains unclear. Here we developed a multilingual brain decoding framework and analysed brain recordings from 227 participants spanning Chinese, English and French. We found that cortical semantic representations exhibited consistent semantic geometry relationships across languages and individuals, revealing a shared cortical semantic geometry. Importantly, the cross-language consistency of cortical semantic geometry increased from sensorimotor to higher-order association cortex, forming a shared semantic geometry gradient closely aligned with the principal cortical functional gradient. Our work establishes generalizable multilingual brain decoding and identifies stable cortical semantic geometry as a neural basis for semantic commonality across languages.

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  • arXiv cs.AIOct 17

    SQuIREL: A Context-Relative Framework for Measuring Entity Richness in Wikidata

    Michelle Nwachukwu, Albert Meroño-Peñuela, Martim Brandão

    Wikidata is a large, heterogeneous knowledge graph and applying data quality metrics requires context-specific approaches. The "richness" quality metric is currently under-researched and typically restricted to the ontology level, which fails to capture entity-specific structural depth. To address this gap, we introduce SQuIREL, a method that measures richness directly at the entity level using six dimensions: Semantic Specificity, Qualifier Density, Internal Connectivity, Reference Provenance, External Network Connectivity, and Linguistic Accessibility. These dimensions are kept disaggregated to preserve actionability for targeted editorial intervention. We apply this metric to two case studies of oil paintings (Brazilian vs. French) and writers (grouped by citizenship and gender) to demonstrate how it surfaces representational inequalities that may indicate systemic bias. Our metric successfully distinguishes between highly tailored and basic entity profiles, revealing nuances that raw counts and existing aggregate scores obscure. Specifically, it identifies structural imbalances, such as 48% of Brazilian paintings clustering as low-richness stubs against 0% in the French…

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  • arXiv cs.AIOct 17

    Spherical Physics-informed Neural Operator with Multi-scale Coupling for Meteorological Downscaling

    Yiqiang Ye, Yichi Wang, Haodi Zhang, Jiawei Wen, Jiahui Jiang +3

    Meteorological downscaling is crucial for high-resolution regional climate forecasting and disaster early warning. While neural operators have emerged as a promising paradigm for modeling complex spatiotemporal mappings, existing frameworks often struggle with spherical manifold geometric distortions, inherent atmospheric multi-scale coupling mismatches, and lack of explicit atmospheric laws. We propose the Spherical Physics-informed Neural Operator, which utilizes a Spherical Laplacian Decomposition to partition atmospheric fields into hierarchical frequency components, maintaining exact point-wise correspondence across scales. To evaluate these representations at arbitrary locations, we introduce a localized spherical integral operator that approximates continuous kernel transforms via geometry-aware attention. Dynamical consistency is further enforced by embedding differentiable constraints into the learning process. Extensive experiments demonstrate that our framework attains superior accuracy and zero-shot generalization across various meteorological variables and unseen queries, representing a robust and interpretable solution for global-to-regional meteorological…

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  • arXiv cs.CLOct 17

    AtomSD: Procedural Atom-Guided Self-Distillation for LLM Agents

    Bohan Yu, Pengfei Cao, Kai Tian, Chen Han, Shi-Yang Li +6

    Large language models (LLMs) possess broad factual knowledge and strong reasoning capabilities, but solving complex tasks also requires procedural knowledge about how to act. Existing approaches often represent procedural knowledge through trajectories or bundled skills, limiting flexible reuse across interaction histories. This paper introduces Procedural Atoms, fine-grained and independently applicable units that represent Intent, When, Do, and Avoid guidance. Building on this representation, we propose AtomSD, an Atom-guided Self-Distillation framework in which the learner continually extracts Procedural Atoms from its own successful trajectories and distills them into the evolving policy through Atom-conditioned supervision and task-reward optimization. Comprehensive experiments show that AtomSD consistently outperforms strong reward-based and distillation methods, exceeding the state-of-the-art baseline by up to 9.31 points in average performance. Further analyses demonstrate that the proposed Procedural Atoms provide effective guidance, highlighting the value of atom-level procedural knowledge for policy learning.

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  • arXiv cs.CVOct 17

    SuperClip Pyramid for Video Temporal Grounding

    Sanghyeok Lee, Juyeon Ko, Joonmyung Choi, Jongha Kim, Dohwan Ko +2

    With the rapid expansion of online video content, video temporal grounding has become essential for localizing moments based on user-specific queries, comprising two subtasks: moment retrieval (MR) and highlight detection (HD). While recent approaches adopt DETR-style decoders or temporal pyramid architectures, they rely on single-scale feature maps or uniformly downsampled representations, limiting their ability to capture semantic boundaries and variable-length events. In this paper, we present SuperClip Video Temporal Grounding (SCVTG) built on a multi-scale temporal hierarchy that better captures distinctive events at various granularities. SCVTG constructs a SuperClip pyramid by grouping adjacent clips into semantically coherent segments, termed SuperClips. Further, to aggregate these SuperClips having variable duration and temporal location, we introduce a temporally adaptive kernel that aggregates SuperClips through temporally conditioned weights, enabling flexible aggregation. On top of this, we design relative saliency losses to guide the model in learning inter-clip importance, enhancing its ability to distinguish relevant events. Extensive experiments show that SCVTG…

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  • arXiv cs.LGOct 17

    Native Probability Outputs for Clinical Triage: An Evaluation of Calibration and Decision Accuracy

    Liang You, Zhiyin Zhou, Dawei Tang, Chuchu Wu, Jingzhuo Hu

    Patient-facing triage requires an appropriate care recommendation and recognition of when information is insufficient. We evaluate whether Jev, a decision model that returns probabilities over predefined actions, supports both judgments. Using CARE-Bench, we compare Jev with four general-purpose language models and three input-independent baselines across 925 decision points from 439 public cases. On the public test subset, Jev ranks fourth of five models by macro-F1 (0.461) but has the lowest observed multiclass Brier score (0.610), an 18.7\% improvement over uniform prediction (case-bootstrap 95\% interval, 4.9--31.1\%). Noul has lower binary Brier than Choice (0.215 versus 0.276). A evaluation of 453 HealthBench conversations again gives Jev the lowest observed Brier score (0.299), but measures a distinct emergency-referral judgment. Jev combines lower probability error with missed clarification needs in the four-action task. Asking about information sufficiency separately increases recall but also produces frequent unnecessary clarification assignments.

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  • arXiv cs.AIOct 17

    Verifying Human-Written Reasoning References

    Ali Ansari, Farhana Ferdousi Liza

    Evaluating the reasoning of AI systems often relies on human-written reasoning chains as references, yet the logical support for their individual steps is rarely verified. Although prior neuro-symbolic approaches have formalized and checked reasoning, their reported evaluations do not systematically assess whether step conclusions in human-written reference derivations are entailed by their cited premise sets or investigate the sources of failures in those references. To address these limitations, we present a novel reference-centred assessment method for human-written derivations that combines entailment checking against cited premise sets, full-premise rechecking, and failure attribution to examine the reliability of these derivations and their formal representations. We evaluate it on P-FOLIO, using a large language model (LLM) to translate step conclusions into first-order logic (FOL) and the Z3 SMT solver to check entailment. Of 386 derivations examined, 202 (52.3%) meet all our checking criteria, meaning that every translated step is entailed by its stated support and the required target is established under the available FOL representations. Among 230 steps not entailed by…

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  • arXiv cs.CVOct 17

    Geometry-Aware Interpolation for Inverse Problems

    Pourya Shamsolmoali, Masoumeh Zareapoor

    Recovering missing visual information from incomplete or degraded images remains a challenging problem, particularly when spatial variations and complex image structures affect reconstruction accuracy. Although Spatial Transformer Networks (STNs) facilitate spatial feature alignment, their reliance on conventional linear or bilinear interpolation limits their ability to capture the underlying geometric characteristics of image data. Furthermore, interpolation based on a restricted set of neighbouring pixels can introduce inconsistencies in gradient propagation, potentially affecting optimisation stability. To address these limitations, we propose a Geometry-Aware Interpolation Network (GAIN), a variational autoencoder-based framework that incorporates manifold geometry into spatial transformation and image reconstruction. Rather than estimating pixel intensities solely from fixed spatial neighbours, GAIN exploits the local tangent structure of the data manifold to construct geometry-informed intensity approximations. An entropy-based regularisation mechanism further guides the estimation of spatial transformation parameters, promoting more adaptive interpolation. In addition, a…

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  • arXiv cs.LGOct 17

    Contrastive Adversarial Training for Robust Graph Neural Networks under Label Poisoning

    Manshika Charvi Bissessur, Melis Ilayda Bal, Michael Muehlebach

    Graph Neural Networks (GNNs) are effective for modeling relational data but are vulnerable to label poisoning, where a small number of corrupted training labels can propagate errors across the graph via message passing. Despite this risk, defenses against label poisoning remain underexplored: existing methods are primarily designed for label noise and often rely on robust losses or heuristic data cleaning that fail to distinguish adversarial poisoning from naturally hard examples. In this paper, we propose CoLAT (Contrastive Label-flipping for Adversarial Training), a novel contrastive adversarial training framework tailored for robust node classification on graphs. The core of our approach is a structure-aware detection model that uses contrastive learning to identify label-structure inconsistencies. Unlike prior contrastive methods that focus on representation learning, CoLAT leverages contrastive embeddings to select high-risk nodes that guide adversarial label-flipping during training. This alternating optimization not only performs structure-aware adversarial training but also implicitly sanitizes some corrupted labels, improving robustness against diverse attacks from the…

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  • arXiv cs.AIOct 17

    MM-LeJEPA: Correlated Gaussian Regularization for Multimodal Joint-Embedding Predictive Architectures

    Haowen Bai, Rui Gong$, Meichen Liu, Ming-Xuan Weng, Bihan Wen +1

    LeJEPA derives an isotropic Gaussian embedding target by minimizing, under its task assumptions, a Fisher information criterion that controls the task-averaged squared prediction bias. This enables unimodal self-supervised learning without reconstruction decoders, exponential-moving-average teachers, or contrastive negatives. However, its isotropic task prior does not account for the cross-modal structure of multimodal tasks that read shared content. We introduce MM-LeJEPA by extending LeJEPA's task assumptions to multimodal learning. Specifically, we model multimodal tasks as strength-weighted combinations of components that read independent information from the two modalities and components that read the same content from both. Under explicit gradient-moment, correspondence, and symmetry assumptions, we derive a correlated Gaussian target that minimizes a weighted Fisher information functional under a fixed total variance constraint. An analytic transform adapts Sketched Isotropic Gaussian Regularization (SIGReg) to this target, yielding a two-term objective that combines distributional regularization with local-to-global prediction. The extension adds a single scalar…

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