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ByteTrail · AI and developer-tools intelligence
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Fields · this week
All fields3077Multimodal531Agents515Frontier LLMs456Inference & serving262Image segmentation180Graph learning1653D scene158Learning verifiable rewards144Voice AI90Classifier & causal discovery85Forecasting time77Flow matching75Teacher policy distillation53Machine learned interatomic53Adversarial training48All 23 fields

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.

Rising research topics

  • Federated learning47+6.8%
  • Machine learned interatomic53−1.9%
  • Forecasting time77−2.5%
  • Adversarial training48−4.0%
  • Anomaly detectors34−5.6%
  • 3D scene158−10.7%
  • Frontier LLMs456−10.8%
  • Agents515−10.9%

Papers

  • arXiv cs.AIAgentsOct 14

    Confidence-Calibrated Inference Expansion for Evaluator-Guided Test-Time Reasoning

    Weida Liang, Kenji Kawaguchi

    Evaluator-guided reasoning presents a local control problem at every step: trust the current answer, return a fallback, or spend more inference. We formulate this choice as a compute-aware three-action controller, scoring terminal actions by predicted answer success and expansion by a rollout-based continuation-success surrogate, with compute cost priced explicitly. A local action-gap analysis characterizes when score errors can change a decision, while an expected regret bound accounts for errors along the realized expansion path. Guided by this analysis, we learn and calibrate action-specific confidence estimates, with local uncertainty penalties for ambiguous decisions. On held-out width expansion and revision tasks, the controller improves observed utility over calibration-selected fixed policies while reducing normalized online compute by about 30% and 16%, respectively. Across four Qwen3-8B seeds, compute is lower than fitted Q in both settings, while utility differences are small and mixed. The results frame evaluator-guided reasoning as resource allocation under uncertainty, rather than a uniformly stronger reasoner.

    AIEvals70PDF
  • arXiv cs.LGAgentsOct 14

    Amortizing Sequential Experimental Design in Reasoning LLMs

    Anita Kriz, Antonin Berthon, Nicolás Astorga, Mihaela van der Schaar

    Despite their extensive pretraining knowledge and strong reasoning capabilities, large language models (LLMs) struggle to adaptively gather information in experimental design tasks. We study how to amortize sequential Bayesian experimental design (BED) into an LLM by explicitly training the reasoning that guides action selection using information-gain rewards. Our approach specifies the experimental generative model independently of the LLM's own beliefs, allowing us to optimize expected information gain under a model of our choosing. We investigate the design choices for amortization, including alternative generative-model constructions, reward estimation, hypothesis-space specification, multi-step objectives, and computational costs relative to training- and inference-time BED baselines. On Twenty Questions, the resulting policies improve information acquisition and task performance, outperform inference- and training-time BED baselines at substantially lower computational cost, and generalize to unseen categories. We further demonstrate positive transfer from Twenty Questions training to Medi-Q, a real-world clinical dialogue benchmark. These results suggest that information…

    AIEvals70PDF
  • arXiv cs.CLVoice AIOct 14

    PEDBench: Can Speech Models Detect Phonetic Errors?

    Zhonghao Shi, Zackary Rackauckas, Jasmine Yip, Savannah Halpern, Simon Campos +7

    Phonetic error detection (PED) aims to identify phoneme-level substitutions, deletions, and insertions and is essential for educational and clinical speech assessment and intervention. Despite recent advances in speech models for phoneme recognition, their ability to reliably detect fine-grained phonetic errors remains unclear because of a lack of high-quality evaluation data. To address this gap, we introduce PEDBench, the first PED benchmark spanning a continuum from natural to controlled speech. It comprises real-world recordings of spoken errors made by children (N=334), scripted error productions by human actors (N=1,188), and synthetic error productions generated using phone-conditioned text-to-speech (N=1,083). All phonetic annotations were transcribed and verified by two phonetically trained annotators. Our evaluation of 13 state-of-the-art speech models shows that they fail to reliably detect phonetic errors, even under controlled conditions with acted and synthetic speech, where acoustic variability is reduced. The models also exhibit low precision, frequently misclassifying correctly pronounced phonemes as errors. Our speech-language models, fine-tuned on synthetic…

    AIEvals80PDF
  • arXiv cs.CLAgentsOct 14▲ 3

    CoTranslator: Reverse to Forward Chain-of-Thought Translator for Reasoning Distillation

    Zinan Tang, Zhuoshi Pan, Kairui Chen, Ze Yan, Shaomian Zheng +3

    Reasoning distillation requires chain-of-thought (CoT) data with correct answers, but obtaining such data efficiently remains challenging. Forward CoT generation (FCG) followed by rejection sampling (RS) produces verified, question-only responses, yet may require many teacher calls for difficult problems. Reverse CoT generation (RCG) is cheaper because it conditions on the known answer, but access to this answer can lead to post-hoc rationalization and a mismatch with the student's answer-free inference setting. We propose CoTranslator, which learns to translate reverse CoT responses toward the distribution of verified forward responses. Trained on a small set of paired reverse and rejection-sampled forward responses, it supports large-scale generation and verification of student training data. Across six student models evaluated on six mathematics benchmarks, CoTranslator improves average accuracy over semantic suppression by up to $5.82$ percentage points. For two 4B students, it retains $95.0\%$ of rejection sampling's average accuracy while using $35.9\%$ of its estimated compute. Without retraining, the same translator transfers to a new mathematics dataset, a different…

    AITraining & fine-tuning70PDF
  • arXiv cs.LGSmall LLMsOct 14

    Task-Phase-Conditioned Expert Routing Is Not a Reliable Proxy for Quantization Sensitivity in MoE FFN Tensors

    Yifan Liu

    We investigate whether per-layer, per-task-phase (prompt/reasoning/response) expert-routing statistics in a Mixture-of-Experts (MoE) language model can guide layer-wise mixed-precision quantization of routed-expert FFN tensors (down/gate and up). To attain layerwise routing statistics, we build a routing-capture and profiling pipeline for Qwen-3.6-35B-A3B in ik_llama.cpp, derive a composite sensitivity score from routing entropy/decisiveness, coefficient of variation (CV), Jaccard/phase-fragility, and top-1 expert strength, and use coordinate descent to tune a layer allocation that respects a fixed average bits-per-weight budget. Notably, the profiling pipeline is applicable to standard top-k MoE architectures. Across independent experimental conditions (about 4.0 bpw flat, about 2.7 bpw steep, and about 2.7 bpw random allocation as a control), reallocation fails to outperform the uniform baseline within each quality tier. In the flat regime, reallocation is statistically neutral (z about 0.9). For the steep tier, the random reallocation performs within 1 SE of the routing-guided reallocation at the same tier, indicating a structural loss at this quality rung caused by mandatory…

    AIInference & serving70PDF
  • arXiv math.AGOct 14

    Linear Independence of Polynomial Compositions with Bounded Input Degree

    Xiaotian Chang, Yacoub Hendi

    We prove the weak composition-independence conjectures of Kohn, Marchetti, Massarenti and Mella. The proof uses deformations of rational curves and a single kernel of the linearized equation. Riemann-Roch gives a contradiction from tangent fields with controlled poles. By settling these conjectures, we prove that a neural network with a general polynomial activation function of sufficiently large degree is identifiable.

    AI10PDF
  • arXiv cs.CLSmall LLMsOct 14

    Wolof-Adapted Industry-Ready Open-Weight Language Models

    Gwénolé Lecorvé, Delphine Charlet, Pierre Adam, Floriane Behanzin, Lionel Delphin-Poulat +13

    Wolof is spoken by 23M people, mostly in Senegal, but remains weakly supported by open-weight language models, while API models raise cost, governance, and sovereignty constraints for local deployments. In this paper, we present 0.6B-14B open-weight models adapted to Wolof through instruction tuning, preference alignment, and model merging. After presenting the pipeline and data, extensive experiments on 63~public and in-house tasks show good performance on Wolof understanding and generation while remaining effective on other languages and cross-lingual skills, with the 8B and 14B models being particularly competitive with substantially larger commercial systems. The models are currently being evaluated in internal customer-care pilots and are publicly available at https://huggingface.co/collections/Orange/wolof-lm .

    AI80PDF
  • arXiv cs.RORoboticsOct 14

    Feasibility Ceilings Before Learning: Sizing and Timing Bounds for Drone-Mesh Interception

    Dmytro Babarytskyi

    A mesh of interceptors defending a fixed site must spread over the predicted arrival point of an approaching object. This is a recurring task in multi-agent robotics: N agents, each covering a disc of radius r, spread over a probability density so that a point drawn from it falls within some agent's disc. We give closed forms for the quantities that bound performance before any policy is chosen, and validate each by Monte Carlo. The uncertainty of a point predicted by extrapolating a noisy linear track follows from least-squares variance propagation plus a delta-method term for the uncertain arrival time, agreeing with simulation to within 1.8%. The coverage probability of a packed mesh admits a closed form accurate to 0.007 absolute, with a volumetric analogue. A naive area ratio misses a ceiling: while agents stay at least 2r apart, no mesh exceeds 90.69% coverage in the plane or 74.05% in a volume. This ceiling belongs to the separation constraint, not to the problem - permitting overlap substitutes Kershner's thinnest-covering density and removes it, the better regime above a crossover at p=0.80. Hexagonal packing is optimal among five arrangements; the centroidal-Voronoi…

    AI50PDF
  • arXiv cs.AIImage & video generationOct 14

    Temporal Specification Guidance for Text-to-Video Generation

    Avishree Khare, Hideki Okamoto, Bardh Hoxha, Georgios Fainekos, Rajeev Alur

    Text-to-video models often generate the visual concepts requested in natural language prompts but fail to satisfy their intended ordering, timing, and persistence requirements. We propose representing these requirements as specifications in a formal Temporal Logic, and introduce Temporal Specification Guidance (TSG), a training-free inference-time framework that turns temporal specifications into executable objectives for guiding video generation during sampling. TSG scores traces of visual concept evidence using differentiable quantitative semantics and uses the score's gradients to construct a specification-guided denoising direction. We instantiate this framework with video-text attention traces, efficient differentiable scores for Metric Temporal Logic, and gradient-derived attention biases. This instantiation requires neither an external concept classifier nor backpropagation through the video transformer. Its attention-steered forward pass yields a denoising direction that augments classifier-free guidance (CFG). On a benchmark of 220 prompts spanning 11 categories of temporal constraints, including ordered object entry and state transitions, this instantiation improves…

    AIMultimodal80PDF
  • arXiv cs.CVMultimodalOct 14▲ 43

    Intern Lumina U2: A Multi-Codebook Diffusion Large Language Model for Omni-Visual Understanding and Image Generation

    Qian Yu, Zongyang Qiu, Juncheng Yan, Hongliang Li, Qi Qin +38

    Unified multimodal generation covers discrete, continuous, and hybrid paradigms. Discrete approaches with single-codebook visual tokenizers align naturally with text modeling but have limited representational capacity. Continuous and hybrid approaches retain richer visual representations, but they model text and images in heterogeneous spaces with different training objectives, potentially introducing optimization conflicts between modalities with misaligned representation spaces. These choices expose a central question: how to efficiently model high-capacity visual representations alongside textual content in a shared optimization space within a single backbone. We present Intern Lumina U2, to our knowledge the first diffusion language model to represent multi-codebook visual tokens in a fully discrete space. Each visual position is represented by a group of codes from complementary codebooks, increasing its representational capacity while preserving compatibility with discrete text modeling. At the input, the embeddings are concatenated at each position; at the output, a lightweight multi-codebook autoregressive head predicts the codes within each position sequentially, while…

    AIImage & video generation90
  • arXiv cs.LGOct 14

    O-RAN-Native Jamming Detection Under Network Heterogeneity

    Sneihil Gopal, Fernando J. Cintrón, Richard A. Rouil

    Radio-frequency jamming threatens cellular network operations in contested environments. The Open Radio Access Network (O-RAN) architecture exposes per-User Equipment (UE) telemetry through the E2 interface, enabling Key Performance Indicator (KPI)-based jamming detection without proprietary hardware. However, operational deployment demands detection methods that generalize across heterogeneous networks. We propose a Statistical Ensemble Method (SEM) combining adaptive Median Absolute Deviation (MAD)-based baseline detection, count-based anomaly detection, and abrupt change detection through a weighted voting system. SEM operates in two modes: a cold-start mode requiring no labeled data for immediate deployment, and a supervised mode where parameters are refined offline via grid search as labeled data accumulates. We evaluate SEM against five machine learning (ML) classifiers and five deep learning (DL) sequence models using approximately 25 hours of testbed data comprising logs from eight heterogeneous 5G environments, employing both single-environment and multi-environment leave-one-out (LOO) validation. Our analysis indicates that under conditions not encountered during…

    AI10PDF
  • arXiv cs.LGTraining & fine-tuningOct 14

    Gibbs-Gap Policy Optimization for Off-Policy RL with Verifiable Rewards

    Xuanchang Zhang, Tong Zhang

    Reinforcement learning with verifiable rewards often trains language models on responses generated by a different, possibly stale behavior policy. Correcting this mismatch with learner-to-behavior importance weights can introduce high variance for long responses. We introduce Gibbs-Gap Policy Optimization (GGPO), which minimizes a log-mean-exp contrast of KL-adjusted rewards among responses to the same prompt, without learner-to-behavior importance weights. We derive GGPO from KL-regularized policy optimization: on policy, its population loss is exactly the KL-regularized optimality gap, and under suitable coverage the learner's single-response expected reward is at least the behavior policy's best-of-$K$ reward, minus terms for the raw finite-group loss, regularization, and finite training data. We characterize reference refresh through exact Gibbs-target composition and a finite-data final-policy reward bound under local reference control and tokenwise behavior mismatch. This refresh is empirically important: in an ablation, fixed-reference GGPO scores $32.96$ on the five-benchmark problem-weighted average, versus $46.58$ with the default refresh schedule. Experiments on…

    AI60PDF
  • arXiv cs.CVMultimodalOct 14

    RIGen: Beyond Action Following with Reciprocal Interaction in Generative Gameplay

    Mengfei Li, Yi Zhang, Xingtong Ge, Lunjie Zhu, Fangyu Lin +6

    Generative gameplay requires more than immediate responses to player actions.Actions should have persistent consequences, while the generated world should act according to its own state and rules, shaping the player's situation and subsequent choices. Video generation captures local dynamics, but limited history, partially observed game state, and the need for contextual behavior decisions make such interactions difficult to sustain through audiovisual prediction alone. We introduce \rig (Reciprocally Interactive Generation), a framework for reciprocal interaction in generative gameplay. \rig combines persistent state and configurable rules, contextual interaction reasoning, and action-conditioned audiovisual generation. Together, these components make interaction consequences and actor behavior jointly determine generated content while maintaining consistency between game state and visible outcomes. We construct a Xonotic gameplay dataset to train an audiovisual generator for learning dynamics and interactions. Further post-training enables \rigarena, an interactive generative first-person shooter (FPS) prototype. Experiments demonstrate more reliable combat outcomes and…

    AIImage & video generation80PDF
  • arXiv cs.AIAgentsOct 14

    A Self-Improving Agent that Grows its Own DSL: Oracle-Free Operator Induction for Exact Question Answering

    Yun-Shiuan Chuang, Yirou Ge, Yixi Li, Yiming Bian, Zijie Pan +4

    A self-improving agent has to decide which new skills to keep, and in practice it keeps whatever earns a high reward, convinces a judge model, or compresses its library, even when the skill is wrong. That is fine until the answers have to be exactly right: over a table of transactions, a total is either right or wrong. We ask a harder question: can an agent grow what it can compute with no reward, no judge, and no answer key, and still be exactly right? A single frozen model runs two nested loops over a small, exact domain-specific language (DSL). The inner loop answers new questions only when two independently written expressions agree on every record. The outer loop discovers its own missing capability and authors an operator for it, admitted only when an independent second implementation agrees and the result obeys a conservation law, all without ground truth. The inner loop roughly triples the answerable-question set, and the outer loop self-extends on disjoint starting sets, inventing operators outside the starting closure; a no-induction control answers none of the new questions. The admission rule is empirically sound: it rejects 138 of 138 buggy operator variants under an…

    AICode models80PDF
  • arXiv cs.CLAI safetyOct 14

    PRISM: Transfer and Limits of Activation Interventions in Tool-Calling Language Models

    Giovana Kerche Bonás, Thales Sales Almeida, Marcelo da Silva Reis, Rodrigo Nogueira, Marcos Meideiros Raimundo

    Recent literature shows that unsafe tool calls can often accompany textual refusals in language models. To investigate this hidden vulnerability, we introduce PRISM, a framework for evaluating the transfer of activation interventions across precision, languages, and scales. Our findings demonstrate that leveraging lower-resource settings (specifically quantized NF4 labels and cross-lingual transfer) offers a robust pathway to expose and improve model alignment. Notably, using behavioral labels from an NF4 copy enables stable intervention estimation even with very few scenarios. Furthermore, these alignment improvements transfer effectively across languages: five-language NF4-supervised directions successfully transfer to held-out Arabic, reducing unsafe violations in Llama-3.1-8B by 21.95 percentage points without target-language tuning. By mapping paired benign activations, we also successfully transfer these safety interventions to much larger models (up to 70B parameters). Ultimately, PRISM highlights how auxiliary supervision from compressed models and multilingual data can uncover and mitigate hazardous tool-calling behaviors across different model families. We release all…

    AIAgentsEvals85
  • arXiv cs.SEOct 14

    Robotic-supported Data Loss Detection in Android Applications

    Davi Freitas, Breno Miranda, Juliano Iyoda

    Smartphones have become integral to modern life due to their diverse applications. However, the development of applications for these devices faces significant challenges, primarily due to two factors: (i) the diverse hardware and operating system versions that require testing across multiple configurations; and (ii) the imperative activity of testing the smartphone in a non-invasive way (similar to an end-user interaction), which is expensive, tedious and error-prone as it is usually carried out manually. In this context, robotic automation has emerged as an effective solution for addressing the diversity of smartphone systems and versions, and in performing some testing tasks non-invasively. In addition, automated robots facilitate quick, accurate, and repetitive testing, thus enabling developers to validate their applications across various configurations effectively. This results in a less invasive verification and in a significant reduction in time spent on manual testing, thus accelerating the development cycle. Our work proposes R-DLD (Robotic-supported Data Loss Detection), a robot-assisted infrastructure for data loss detection in Android applications, offeringless…

    PDF
  • arXiv cs.AIRAGOct 14

    When Reasoning Traces Conflict with Evidence: How Answer Configuration Shapes Evidence Following

    Shaoang Li, Yanhang Shi, Jian Li

    A reasoning trace retained at answer time plays two roles: it can raise accuracy, and it can compete with the current evidence when the two disagree. We disentangle these roles by crossing verified evidence versions with the traces generated from each and answering from the same traces and evidence under two configurations: continuation of the model's own thinking and a separate answer call. Across three model families (gpt-oss, Gemma4, Qwen3) on HotpotQA and MuSiQue, the separate call raises evidence-following over continuation by 29.9 to 60.6 points. On the same pairs, removing the trace lowers accuracy by similar amounts under both configurations. Yet continuation repeats the stale conclusion in most conflicts, and the separate call follows the current evidence far more often. Benefiting from a trace and deferring to it are therefore distinct properties. Further analyses show that the final line of a trace carries most of its value, gold evidence helps most before the trace is written, distractors weaken correction under a separate call, and renewed reasoning, unlike a verification instruction, follows the current evidence in most conflicts. Because trace deletion alone cannot…

    AIEvals70PDF
  • arXiv cs.LGOct 14

    Optimal Explicit and Implicit Regularization Strengths in High-Dimensional Continual Linear Regression

    Gilad Karpel, Daniel Soudry

    We study continual linear regression in a high-dimensional regime, where a learner observes a sequence of tasks and updates its parameters sequentially. In this setting, we derive an explicit closed-form expression for the generalization loss of a model trained with gradient descent for a fixed number of steps per task, which holds for arbitrary teachers. We prove that the optimal fixed step size decays nearly inversely with the number of tasks, specifically as $\ln T / T$, for a single teacher and, more generally, i.i.d. teachers. Under this scaling, we show that the resulting generalization loss is asymptotically equivalent to that achieved by optimally tuned L2 (isotropic) regularization. Furthermore, we extend our analysis to generalized ridge regression with diagonal regularization matrices, obtaining a closed-form characterization of the generalization loss and identifying necessary and sufficient conditions under which the optimal regularizer is isotropic. Finally, we identify a regime where the optimal step size remains finite even as the number of tasks tends to infinity, thus revealing a non-trivial tradeoff between task variability and implicit regularization, in…

    AI20PDF
  • arXiv cs.CRAI safetyOct 14

    Privacy-Preserving Text Inference for Health-Misinformation Classification and Semantic Retrieval

    Jasleen Kaur, Chloé Cachet, Koray Karabina, Jean Belo Klamti, Philippe Lamontagne

    Health-misinformation analysis presents a dual challenge: misleading claims can affect public well-being, while systems designed to detect them may expose sensitive user-submitted text. This motivates privacy-preserving inference for public-health applications in which user queries may contain confidential medical information and trained models and reference databases are maintained on organizational servers. We design a privacy-preserving text inference framework based on CKKS homomorphic encryption for health-misinformation classification and semantic retrieval. The classification pipeline fuses BioBERT embeddings, PCA-reduced MPNet embeddings, and SVD-reduced TF-IDF features, while the retrieval pipeline combines MPNet embeddings with ISCC-SCT semantic fingerprints through late fusion. We evaluate predictive performance and benchmark plaintext versus ciphertext-based execution. The hybrid classifier achieved an accuracy of 0.971 and a macro-F1 score of 0.950, with identical reported metrics under plaintext and ciphertext-based inference. The hybrid late-fusion similarity model achieved a Pearson correlation of 0.8691 with human sentence-pair similarity scores on STS-B. For…

    AIRAG70PDF
  • arXiv cs.LGOct 14

    EEG-Based Prediction of Music Enjoyment Using Deep Learning with Explainable Temporal Analysis

    Parshav Singla, Simranjit Kaur, Maanya Walia, Niharika

    Understanding why listeners enjoy music is difficult because the experience is subjective and changes as a piece unfolds. Electroencephalography (EEG) offers a non-invasive way to study neural responses during listening, yet earlier work has mostly treated music emotion as a categorical label or relied on black-box models. We formulate enjoyment prediction as a regression problem on the public MUSIN-G dataset, which contains 128-channel EEG from 20 listeners together with song-level enjoyment ratings. Six deep learning models were compared under a participant-wise protocol, in which 15 listeners were used for training and validation and 5 were held out for testing. DeepConvNet, CNN-LSTM and GNN-CNN reached the lowest mean absolute error (0.97 to 0.99 on the 1 to 5 scale), while the temporal convolutional network (TCN) had the highest Pearson correlation with the ratings (r = 0.21). Gradient-based and occlusion-based explanations, averaged over all held-out windows, identify the time segments within each two-second window that most influence the TCN predictions. The study provides a reproducible cross-listener benchmark for EEG-based enjoyment regression and shows how…

    AI10PDF
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