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All fields3151Multimodal507Agents487Frontier LLMs421Inference & serving254Image segmentation1703D scene153Learning verifiable rewards146Graph learning142Voice AI94Classifier & causal discovery83Forecasting time75Flow matching68Evals65Federated learning48Teacher policy distillation47All 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

  • Training & fine-tuning44+120.0%
  • AI safety31+93.8%
  • Computer vision33+83.3%
  • Evals65+51.2%
  • Code models14+40.0%
  • Federated learning48+29.7%
  • Image & video generation22+29.4%
  • Robotics18+28.6%

Papers

  • arXiv cs.LGOct 16

    Bootstrap Stochastic Interpolant Tree for Inference-time Search Under Sparse Reward

    Binglin Ji, Anindya Sarkar, Yevgeniy Vorobeychik

    Tree-based samplers offer a compelling alternative for scalable inference-time search, bypassing the per-step reward and gradient dependencies that introduce systematic bias into SMC-style frameworks. Despite this advantage, contemporary tree samplers fail to achieve broad exploration. Their reliance on learned, DDPM-like transitions inherently couples neighbor states in a trajectory, thereby restricting child-parent feature divergence in the tree sampler. Consequently, these samplers suffer from restricted coverage of the target marginal distribution, severely hindering the exploration critical for efficient search under sparse reward. To overcome these limitations, we introduce a simple yet effective Bootstrap Stochastic Interpolant Tree (BSIT) sampler that decouples child node generation from explicit parent dependencies. By expanding the degrees of freedom during child node sampling, BSIT encourages diverse trajectory rollouts and unlocks broader path-space diversity-the exact catalyst needed for efficient exploration. Crucially, we show that BSIT's improved exploration accelerates convergence to the target distribution while achieving broader distributional coverage than…

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  • arXiv cs.CLOct 16

    Multi-author style detection with stylistic embeddings

    Man-Ling Sung, Chak Fai Li, Damianos Karakos, Fred Bernardin, Brian Ulicny

    We investigate the use of Large Language Models (LLMs) in multi-author style change detection. Using off-the-shelf LLMs or supervised fine-tuning solely within the target domain fails to yield effective features; for this reason, we propose cross-domain transfer learning with LLM-based embeddings fine-tuned for authorship verification. We fine-tune Mistral-7B embedding model and compare it with DeBERTa, used in the state-of-the-art (SOTA) systems in PAN-25 multi-author writing style analysis challenge. Results show that DeBERTa and Mistral excel in different user-generated text scenarios. We exploit this diversity by combining both embeddings, leading to more effective style change detection.

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  • arXiv cs.CVOct 16

    Precision Cotton Disease Management: Leveraging UAV Imagery and Custom YOLO Deep Learning Framework for Enhanced Detection

    1. Anwar Iqbal*1 aiqbal. rime23smme@student. nust. edu. pk 2. Kunwar Faraz Ahmad Khan1 kunwar. faraz@smme. nust. edu. pk 3. Shahbaz Khan1 shahbaz. khan@smme. nust. edu. pk 4. Khawaja Fahad Iqbal1 fahad. iqbal@smme. nust. edu. pk 5. Sara Ali1, 2 sara. baber@smme. nust. edu. pk 6. Saqib Nazir1 saqib. nazir@smme. nust. edu. pk 7. Muhammad Azhar Javed3 muhammadazhar6231@gmail. com 1 Department of Robotics, Artificial Intelligence, SMME, National University of Science +9

    Cotton sustains the textile economies of over 80 countries with an annual yield of 113 million bales, yet 30-60% of yield losses are still attributed to viral, bacterial, and fungal diseases that remain undetected until manual field scouting. To address this critical issue, the integration of Unmanned Aerial Vehicle (UAV) imagery with deep learning offers a scalable early-warning system; however, existing models suffer from low early-stage accuracy, poor transferability, and high computational cost. In this paper, we propose Cotton-Vision, a fully convolutional single-stage detector that synergizes three lightweight architectural innovations: Ghost Bottleneck modules for a 20% parameter reduction, Efficient Channel Attention (ECA) for adaptive feature recalibration, and Cross-Stage Partial (CSP) fusion for enriched gradient flow. The network is trained on a novel dataset collected across seven cotton fields in the Punjab province of Pakistan, comprising over 2,700 high-resolution drone images. Extensive data augmentation (rotation, shear, exposure, and saturation) expands the dataset to 11,704 training samples. On a held-out test set, Cotton-Vision achieves a precision of 97.7%,…

  • arXiv cs.AIOct 16

    Self-Improving Video-to-Action Models

    Hyunwoo Ryu, Eric Ming Chen, Max Wilcoxson, Vincent Sitzmann

    Video generative models are an emerging tool for embodied agents: they can serve as scalable visual planners, generating videos that depict how to complete previously unseen tasks. However, executing these plans typically requires a video-to-action or "inverse dynamics" model trained on large, human-collected video-action datasets, severely limiting scalability. We introduce Mirror, a self-supervised approach that learns video-to-action mappings through environment interactions, without prior action data or hand-crafted rewards. Mirror learns by attempting to follow a visual plan by executing its best guess of the underlying actions in the environment. Mirror then trains on the resulting video-action pairs, incrementally self-improving and following the visual planner more closely. Across Atari, OGBench, and DeepMind Control, Mirror outperforms leading learning-from-observation methods with learned video plans, and achieves near-expert performance with oracle plans. On Minecraft BASALT, it outperforms methods with access to expert actions or hand-engineered task knowledge. In Minecraft survival, it is the first method to acquire an iron pickaxe through self-improvement alone,…

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  • arXiv cs.CVOct 16

    SERO: Semantic Prediction for Single-Step Object Removal

    Amreen Hasheem Siraj, Alireza Javanmardi, Pragati Jaiswal, Didier Stricker, Alain Pagani

    Object removal and semantic inpainting of missing regions remain inherently ill-posed, as multiple plausible completions may exist for a given scene. Recent diffusion-based methods have demonstrated strong potential for high-quality object removal; however, they still face two key limitations. First, although they can synthesise sharp and high-resolution images, they often struggle to complete removed regions in a semantically consistent and contextually meaningful manner, where the generated content should agree with the surrounding scene layout and object relationships rather than merely appear visually realistic. Second, their iterative denoising process makes them computationally expensive, limiting their practicality for downstream applications. We propose SERO, a semantic prediction framework for single-step object removal. Our model predicts a hidden-region semantic prior in embedding space, which guides a latent bridge-matching generator toward plausible scene completion by providing both where to edit and what structure is likely to exist behind the removed object. This enables visually realistic and contextually coherent completions in one forward pass. Experiments on…

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  • arXiv cs.CLOct 16

    Prosodic Structure as a Critical Component in Phonemic Language Modelling at BabyLM Scale

    Bill K. S. Ku, Janet B. Pierrehumbert

    Human children learn language from continuous, prosodically structured speech long before they can read, yet almost all language models train on orthographic text. Building on previous phonemic BabyLM training (Goriely et al., 2024; arXiv:2410.22906), we ask whether supplying an estimate of the prosody infants exploit improves phonemic language modelling at BabyLM scale. We compare an orthographic baseline to three conditions forming a ladder of phonological richness. All conditions are trained as BERT MLMs at 10M and 100M words. Addressing the ecological validity of existing metrics in the small-data regime, we build a lexical decision task and two semantic organisation tasks. Our new lexical decision task offers improved insights into the distinction between wordlikeness, token familiarity, and lexicality as factors. Triphone phonotactic control and pseudo-log-likelihood scoring shows that these scores reflect phonotactic familiarity rather than wordhood: every model sits near or below chance on an adversarial inverted condition, revealing prior results of this kind as partly artefactual. On our new semantic tasks, a prosodically annotated phoneme stream gives the strongest…

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  • arXiv cs.MAOct 16

    Model Capacity Frontier: A Development Framework for LLM-Supported Social Simulation

    Patrick Zhao, Yuxuan Li, Hirokazu Shirado

    Large language model (LLM) agents are increasingly incorporated into agent-based models (ABMs) on the premise that richer, context-sensitive human behavior can improve empirical fidelity. However, LLM-supported ABMs introduce an open-ended design space in which subtle modeling and configuration choices, such as prompt design and environment coupling, can substantially alter outcomes. Developing LLM-supported ABMs to simulate human collective behavior therefore requires navigating configuration sensitivity while identifying the limitations of the current model architecture. In this paper, we introduce the \textit{Model Capacity Frontier} (MCF), a framework that characterizes the empirical performance attainable by a specified architecture across its admissible configurations. In multi-objective settings, this capacity is represented by an estimated Pareto frontier obtained through systematic configuration exploration. We demonstrate MCF in crowd simulation of university commencement events with approximately 12,000 agents, using empirical movement data on temporal occupancy, spatial distribution, and flow direction. We first characterize the capacity of a conventional crowd model…

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  • arXiv cs.LGOct 16

    Margin Certificates for Voting Classifiers under Mixing Dependence

    Adria Binte Habib, Ibne Farabi Shihab

    Margin bounds explain why weighted votes generalize after training error vanishes, but dependent observations require concentration arguments that keep the dependence model and failure budget explicit. We study stationary mixing streams without treating dependence as a formal substitution of an "effective sample size." For a finite base class, we combine independent-block Rademacher theory for stationary $β$-mixing processes with a clipped margin loss to obtain an explicit uniform voting-margin certificate, whose dependence penalty is set by the number of approximately independent blocks and the coupling budget $4(μ-1)β(a)$. A black-box transfer theorem shows that any i.i.d. voting-margin guarantee remains valid on a regularly spaced subsequence, with only an additive $β$-mixing failure probability. For votes fixed independently of a stationary finite-state reversible Markov stream, spectral concentration gives the cleaner $\sqrt{t_{rel}/n}$ rate, and a sticky-refresh construction shows this exponent is unavoidable. The constant is statistic-specific: the effective sample size is $n/τ_{int}(g)$, with $τ_{int}(g) \le 2t_{rel}-1$. When the dependence scale is unknown, a batch-means…

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  • arXiv cs.CLOct 16

    Pronouncing Hard Words: Phoneme Adaptation of a Pretrained Conversational Text to Speech Model

    Yashish M. Siriwardena, Dinesh K. Gnanasekaran, Tanmay Laud, Shinji Watanabe, Subhabrata Mukherjee +1

    Voice agents built as cascaded automatic speech recognition (ASR)->large language model (LLM)-> text-to-speech (TTS) pipelines increasingly rely on conversational TTS: streaming, context conditioned models that adapt delivery to dialogue history. Such models are natural and expressive but offer little explicit control over how rare, trust-critical drug names are pronounced, because these entities are too infrequent, and their vocabulary too fast-growing, to be covered by any amount of pre-, mid-, or post-training. We retrofit phoneme controllability onto a pretrained conversational TTS model without any pre/mid-training, using an ARPAbet vocabulary exten-sion, continued phoneme-augmented supervised fine-tuning (SFT), and a training-free inference-time acoustic-biasing step. On a 7,500 clip clinical drug-pronunciation evaluation, our model raises strict Gemini-judged correctness from 54.6% (no phoneme augmentation) to 90.7%, outperforming every conversational and stateless TTS baseline on the automatic metric. Because no training stage sees a drug name, the entire evaluation is out-of-domain, so the gains reflect learned pronunciation rather than memorization. In a blinded,…

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  • arXiv cs.AIOct 16

    TorchTPU - A Hybrid Eager/Compiled Architecture for Native PyTorch Execution on TPU

    Claudio Basile, Michael Voznesensky, Gunhyun Park, Ben Wilson, Parker Holloway +48

    TorchTPU is an open-source collaboration between Google and Meta that introduces a native and principled PyTorch backend for Google TPUs. It enables seamless out-of-the-box model execution and TPU migration while delivering industry-competitive inference and training performance through eager execution optimizations, torch.compile integration, and native hardware acceleration.

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  • arXiv cs.CLOct 16

    CoDA: A Human-LM Collaborative Codebook Development Assistant for Domain-Specific Text Annotation

    Jaspreet Ranjit, Swaminathan Chellappa, Augusto Rivas Costante, Chandan Manjunath, Laura Onasch-Vera +3

    We present CoDA, an open-source human-LM collaborative codebook development assistant that helps domain experts develop annotation guidelines for annotating new variables in unstructured text with language models. Existing language model annotation systems primarily focus on downstream classification performance, while assuming the availability of high-quality annotation guidelines, or codebooks. In practice, developing these codebooks is time-consuming and challenging, particularly for concepts that require expert knowledge. CoDA addresses this gap by collaborating with domain experts to iteratively develop codebooks through targeted review of incorrect language model predictions and incorporating expert feedback into the evolving codebook. We demonstrate CoDA on two real-world case studies: extracting health indicators from homelessness case management notes and classifying sentiment of wildlife trafficking posts on social media. Across both domains, CoDA produces codebooks that achieve downstream classification performance comparable to expert codebooks while reducing codebook development time from several weeks to two hours and requiring 5X fewer manually annotated examples.…

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  • arXiv cs.LGOct 16

    Budgeted Bayesian Bandits for Future Utilization

    Sudipto Guha

    The multi-armed bandit problem models the trade-off between exploration and exploitation. In this paper, we study the variant where exploration has a hard budget and precedes exploitation. Prior guarantees include a 1/3 approximation by Guha and Munagala using Lagrangian duality, and a 4/27 approximation by Ma against a primal linear programming relaxation for a more general model. We show that the analyses of both algorithms are tight, even when their relaxations are asymptotically exact. We provide a different efficient algorithm that achieves a 0.43 approximation. We then provide an efficient algorithm whose guarantee approaches 1-e^{-1} approx 0.63 for bounded exchangeable reward observations in a non-vanishing regime, which is asymptotically tight against natural linear programming relaxations. The technique also gives certificates for stopping sequential model evaluation.

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  • arXiv cs.CVOct 16

    NV-Generate-Body: 3D Latent Diffusion Model forControllable Anatomy, Tumor, and Demographic Body Mask Synthesis

    Can Zhao, Pengfei Guo, Dong Yang, Yufan He, Yucheng Tang +6

    Curating large labeled CT datasets is expensive and constrained by patient privacy. We present NV-Generate-BodyMask, a 3D latent diffusion model that synthesizes whole-body CT segmentation masks with precise, joint control over organ anatomy, tumor burden, and patient demographics. The model operates in the compressed latent space of a frozen mask autoencoder, generating $256^3$ volumetric label maps conditioned on a 19-dimensional vector via Adaptive Group Normalization and cross-attention. A size-adaptive ROI loss with spatial halo weighting and a paired tumor injection pipeline enable accurate tumor size conditioning despite the scarcity of annotated tumor cases. Trained on 8,571 masks across 26 cohorts spanning 132 labels, the model achieves Spearman $ρ= 0.70$--$0.92$ for organ size, 85--100\% tumor recall for lung, pancreatic, colon, and bone lesions, and strong demographic alignment (BMI $ρ= 0.78$, sex-conditioned perfect prostate control).

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  • arXiv cs.CLOct 16

    Preservation and loss of algorithmic structure in experimental diffusion of joint-action norms

    Dhara Yu, Bill D. Thompson

    Everyday norms of social interaction - e.g. norms of sharing space, taking turns, or queuing - are essential to large societies, offering structured procedures for common interactions. However, it remains unclear how the underlying algorithms of these implicit joint plans evolve as they diffuse through populations. Here we show that this process of diffusion can select against complex algorithms in the earliest stages. We conducted a large-scale study of experimental diffusion of novel social norms among human participants, leveraging advances in program synthesis to characterize the procedures people developed in independent populations. Inherited procedures became structurally simplified and lost during interactions between participants who inherited different variants of joint-action norms; however, opportunities for interactive repair offset this pressure, preserving complex algorithms despite misalignment. These results demonstrate that interactive reasoning mediates selective pressures on structural complexity during cultural evolution of procedural norms.

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  • arXiv stat.MEOct 16

    lepidoptera: A Domain-Agnostic Engine for Causal Simulation with Mixed Variable Types, Vine Copula Dependence, and Individual-Level Counterfactual Propagation

    Danilo Augusto Sarti

    lepidoptera is an open-source R and Python package for simulation from structural causal models with heterogeneous variable types. The package is built around a typed mechanism architecture, which separates the causal graph from the generating rule at each node and exposes each rule as an inspectable, serialisable object. A pre-built mechanism library covers all nine combinations of continuous, categorical, and ordinal parent and child types, removing the need for users to implement type-specific sampling from scratch. A vine copula mechanism estimates the full joint dependence structure from observed data using pair-copula constructions and the Rosenblatt conditional-simulation algorithm, supporting over twenty copula families. Individual-level counterfactual queries are supported through partial propagation, which freezes the random draws of unaffected nodes and replays only the downstream consequences of an intervention, thereby separating the individual treatment effect (ITE) estimand from the population-level average treatment effect (ATE) without additional model assumptions. An extension layer allows any pre-fitted predictive model to serve as a generative mechanism via a…

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  • arXiv eess.SPOct 16

    Sequence-to-Sequence Neural Surrogate Modeling of Transient Responses in High-Speed Links with Variable Design Parameters

    Julian Withöft, Shayan Ghanbari, Werner John, Emre Ecik, Ralf Brüning +1

    This paper presents sequence-to-sequence (seq2seq) neural surrogates for high-speed transient response modeling and signal integrity analysis in fixed and variable design parameter spaces. To identify the structural requirements for prediction accuracy, multiple neural architectures were evaluated with respect to different training strategies such as sample-based and unit interval-based, as well as input configurations such as TX-only and TX+RX. For fixed design parameters, feedforward neural networks achieve accuracy comparable to recurrent models while requiring lower computational cost, as the static mapping can be approximated by fully connected and non-sequential representations. Variable design parameters alter the electromagnetic characteristics of the interconnect, which creates parameter-dependent temporal dynamics that require sequence-aware architectures to resolve the evolving interactions within the transient response. The results based on a 14-dimensional variable design parameter scenario show that while autoregressive TX+RX feedback suffers from severe phase drift and error propagation under variable conditions, non-autoregressive TX-only inference leads to…

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  • arXiv cs.SDOct 16

    From Spectrograms to Shallow Speech SSL: Audio-Frontend Design for Generator-Disjoint and Low-Latency Audio--Visual Deepfake Detection

    Qiwu Wen, Jahangir Alam, Eric Charton

    Video-mediated conversational systems rely on synchronized speech and facial video, making audio--visual input integrity important for reliable perception. We study audio-frontend design in a fixed MRDF (Modality-Regularization-based DeepFake)-based pipeline with AV-HuBERT visual processing and an MSCT (multi-scale cross-modal transformer) fusion encoder. We compare spectrogram-style log-filterbank features with frozen wav2vec~2.0 and WavLM representations, dual-SSL fusion, executed depth, and shallow-layer aggregation. Across three training runs, full wav2vec~2.0 improves mean macro AUROC from $84.19\%$ to $87.19\%$ on generator-disjoint MMDF. Six-layer execution further improves detection, and learned non-uniform aggregation adds $2.53$ percentage points, reaching $90.61\%$. Shallow execution reduces measured inference latency relative to full-depth wav2vec~2.0 across the tested input durations; first-second evaluation reaches $86.85\%$ macro AUROC. In a single-run comparison, dual-SSL concatenation outperforms either single stream, while one-way cross-attention yields lower detection performance and higher latency than concatenation. Layer-weight analysis shows greater…

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  • arXiv cs.LGOct 16

    Continual Test-Time Learning with Evolution Strategies

    Guangya Hao, Yiting Duan, Longyan Tan, Yunbo Long, Hao Wu +3

    Large language models increasingly operate in open-ended environments, where they are expected to continually adapt to evolving tasks and distributions, often without access to external supervision. Existing continual learning approaches rely on external supervision, such as labeled data, demonstrations, or reward signals, which can be costly or unavailable in practice. Test-time adaptation removes this dependence by learning from self-generated supervision, but existing methods derive that supervision from the single policy being updated, so once the policy drifts, its own learning signal drifts with it, leading to policy collapse, degraded reasoning, and catastrophic forgetting. In this paper, we propose \textbf{Evolution Strategy Reinforcement Learning (ESRL)}, a continual test-time learning framework that draws its self-generated supervision from a population of policies instead of the single policy being trained. ESRL perturbs an anchor policy in parameter space to form a population of nearby policies, samples one trajectory from each, and aggregates their answers into label-free group-relative learning signals for the central student. The anchor is held fixed within each…

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  • arXiv cs.LGOct 16

    Inside the Loop: A Mechanistic Study of Weight-Tied Transformers on Depth-Bound Algorithmic Tasks

    Magnus Sesodia, Juil Sock, Philip Torr, Christian Schroeder de Witt

    Solving algorithmic tasks with many sequential steps typically requires scaling up transformer depth or chain-of-thought length, incurring unfavorable parameter and inference-compute costs, respectively. Looped transformers, which reuse one block of layers across many iterations, are poised to address this, since they scale effective depth without adding parameters or tokens. Yet, it remains unclear what each iteration is actually computing, or whether the loop merely simulates a deeper untied stack. Here, we study directed permutation traversal, in which a model follows $H$ hops from a starting node and reports the node it reaches, so every intermediate hop has a known ground truth. We find that a minimum effective depth is necessary to solve the task, beyond which the degree of weight tying becomes the determining factor. For instance, a looped transformer with a four-layer block iterated eight times solves the task in every seed, while an identically-trained untied transformer of the same effective depth never does, succeeding only with a lower learning rate or supervision of its intermediate layers. Probing and intervening on the residual stream, we find that the looped model…

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  • arXiv cs.AIOct 16▲ 6

    A Conditional No-Hair Theorem for Transformers: How Language Models Forget Their Prompt

    Shuyang Xiang

    We ask how much an autoregressive transformer retains of its prompt after generating $t$ tokens. Consider two different prompts followed by the same continuation text, so that any difference in their hidden states comes from the prompt alone, and we find that the distance between their hidden states decays approximately as a power law $t^{-α}$, where $α=1$ is the rate at which a fixed prompt's share of uniform attention shrinks and smaller values mean longer memory. Trained models forget fast ($α\approx0.55$--$0.7$), untrained ones slowly ($α\approx0.2$--$0.5$). We prove that this offset is $O\big(P(\log t)^{L-1}/t\big)$ for a prompt of length $P$ and depth $L$. The bound holds for finite-depth pre-LN softmax transformers with bounded attention logits, under one additional condition: the pre-norm activations stay away from zero. The mechanism is geometric: the prompt's direct influence is diluted as the context grows, and \emph{relay} --- continuation tokens passing on what earlier tokens read from the prompt --- can only add logarithmic factors. This is what we call the \emph{conditional no-hair theorem}: given the text generated so far, the prompt's influence on the next token…

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