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All fields3521Multimodal548Agents525Frontier LLMs446Inference & serving268Image segmentation182Learning verifiable rewards1663D scene162Graph learning161Evals105Voice AI102Classifier & causal discovery87Forecasting time78Flow matching75Training & fine-tuning64Teacher 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.

Papers

  • arXiv cs.LGOct 18

    Mining the Self-Rewriting System: A Vision for Data Mining on AI-Native Infrastructure

    Mayank Darbari, Chiman Salavati, Liang Wu, Dawei Wang, Jiong Zhang +3

    Classical data mining treats data as generated outside the learner. Emerging AI-native infrastructure invalidates this assumption: as agents observe system state, act, update retrieval stores and models, and promote new artifacts into the serving path, their own decisions reshape the future logs, traces, corpora, and evaluation records used for later training and release. We call this new research object a self-rewriting system and the resulting system-induced distribution shift endogenous drift. Our central, falsifiable claim is that cross-surface causes are not identifiable in general from marginal per-surface monitoring: different coupled failures can produce the same individual signals while differing in their joint behavior, requiring joint analysis for reliable attribution. This agenda comprises four foundational research problems: fusing heterogeneous drift signals into calibrated gating and attribution (C1); mining agent traces whose activity alphabet evolves (C2); maintaining knowledge fabrics through online reindexing, faithfulness, and compression decisions (C3); and safely promoting concurrent learning streams through one shared evaluation gate (C4). These remain…

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  • arXiv cs.DBOct 18

    MemDerive: A Diagnostic Benchmark for Agent Memory over Evolving Data

    Sen Gao, Yongan Li, Han Chen, Bingsheng He, Ming Wu +3

    Long-running agents rely on memory of their past interactions for task state, such as what has been completed and which conditions still hold. Such state is usually a derived state. No record states it, and it must be computed by rules from multiple records of an evolving history. Existing memory benchmarks score only the final answer, with no reference for the intermediate states behind it. A wrong answer therefore cannot be traced to the state that was missed, and a state that silently became outdated goes undetected. We present MemDerive, a diagnostic benchmark for agent memory over evolving data. It specifies each scenario symbolically, derives exact gold answers with Datalog, records the gold derivation DAG and the supporting events of every query, and renders the history in natural language with fictional entities and distractors. Its 10 scenarios contain 4,692 events and 1,503 queries whose difficulty varies along access, evolution, and composition. On this ground truth, MemDerive evaluates answer reliability, diagnoses failures by probing the nodes of the gold derivation DAG, and tracks whether answers stay correct as events arrive. Our evaluation of four memory systems…

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  • arXiv cs.SDOct 18

    Whistle and Wrong Lyrics: Synthetic Queries Are Not Enough for Real-World Query-by-Melody

    Luigi Rivolta

    People look up a song by humming it, but also by whistling it or singing it with the wrong words. Published query-by-humming systems and benchmarks focus on hums and sung queries, and no public dataset pairs whistles or wrong-lyrics singing with song recordings. We study both query types with a melody-only retrieval system: a key-normalized pitch track embedded by a 5.2M-parameter contour encoder, with a spectral-peak tracker for whistles. First, a physics-grounded generator renders synthetic whistles and nonsense-syllable singing from the vocal melodies of 11,083 songs. Training on them raises top-1 retrieval of held-out synthetic queries against an 11,050-song gallery from 0.180 to 0.251 for whistles and from 0.193 to 0.264 for wrong-lyrics singing, but the gain does not transfer: on the MLEnd test performers the best synthetic-only model gets 210 of 339 real whistles right at top-1, against 214 for the baseline. Second, a closed-set fine-tune on real MLEnd whistles with performer-disjoint splits raises whistle-to-hum top-1 from 0.631 (95% CI 0.581-0.681) to 0.673 (0.622-0.723), on known songs only. Third, two further generations of synthetic queries also fail to transfer:…

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  • arXiv cs.LGOct 18

    Multi-site Learning for Forecasting Day-Ahead Wind-Power Production for Onshore Wind Farms in Great Britain with Deep Neural Networks

    James Kean, Aidan O'Sullivan

    Accurate day-ahead wind-power production forecasting is increasingly important for energy system operations, market activity and balancing Great Britain's grid as wind generation grows. In this study, we train multi-site deep neural networks (DNNs) using pooled historical production and reanalysis weather data from 88 onshore wind Balancing Mechanism Units (BMUs). Forecasts are produced at 09:00 UK time for the following midnight-to-midnight delivery day, matching NESO's operational day-ahead forecasting schedule. We use reanalysis weather as future covariates during training and numerical weather prediction (NWP) forecasts during inference, extending training beyond the available historical NWP archive. Our multi-site recurrent neural network outperforms site-specific RNNs and a multi-site XGBoost model, while processing the full future-weather trajectory for the forecast horizon further improves accuracy relative to hour-aligned approaches. Our best model, an RNN with a bidirectional weather encoder (BiRNN) for NWP trajectory processing, reduces MW mean absolute error and root mean squared error relative to NESO by 17.94% and 12.51%, respectively, across 638,246 matched…

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  • arXiv cs.CVOct 18▲ 16

    Painting Change Map as guidance for generating painting processes

    Markus Pobitzer, Chang Liu, Niccolò Biondi, Nicu Sebe

    Digital art generation has undergone a massive paradigm shift, moving from static image synthesis to dynamic video generation that can mimic the human painting process. While existing models can synthesize visually stunning time-lapses of paintings being created or "unpainted," they largely treat the creative process as a black box. Current conditioning mechanisms are either too rigid, relying on static geometric cues like Canny edges or depth maps, or too holistic, utilizing simple scalar progress variables that offer no localized control. Consequently, users lack the agency to steer how a painting evolves over time. In this work, we argue that true control over the video generation of artwork requires a joint understanding of two fundamental dimensions: where an artist acts, and when those actions take place. To achieve this, we introduce the Painting Change Map, a novel spatio-temporal control mechanism that explicitly tracks normalized structural changes across a 1,000-timestep continuum, a resolution sufficient to capture fine-grained stroke-level changes while remaining computationally tractable. By mapping localized visual differences into an encoded RGB space, our…

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  • arXiv cs.CROct 18

    GreenNIDS: A Benchmark Workflow and Analysis Dataset for NetFlow Intrusion Detection Experiments

    Lilia-Eliana Popescu-Apreutesei, Sabina-Cristiana Necula

    Comparing the predictive performance and computational cost of flow-based intrusion detection experiments requires both quantities to be available for each run. GreenNIDS connects benchmark execution on public NetFlow datasets to a reusable analysis dataset. Model implementations write a common set of outputs, which are ingested into a run-level table with companion tables for model configuration, feature importance, and training history. The run-level table combines predictive metrics with timing, throughput, memory use, and energy estimates based on thermal design power. A CPU-only demonstration comprises 216 runs across four dataset families, three versions, and binary and multiclass tasks. Ingestion produces a 216 x 96 analysis table with no duplicate run identifiers and complete Matthews correlation coefficient (MCC) and F1 coverage. Multiclass ROC-AUC is missing for 36 PyTorch runs, making metric availability an explicit part of subsequent comparisons. The stored records support grouped performance and training-time plots, as well as resource screening after execution. Combining task-specific MCC thresholds with limits on training time, peak RAM, and estimated training…

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  • arXiv cs.CLOct 18

    Aligning Cultural Behaviors in Arabic Language Models via Preferences

    Junhong Liang, Daniil Orel, Saeed Almheiri, Bilal Elbouardi, Younes Samih +9

    Arabic LLMs have improved rapidly, but aligning them with country-specific cultural behavioral preferences remains underexplored. Previous work has focused primarily on instruction-tuning for factual knowledge, overlooking nuanced cultural behaviors. We propose ArabPref, which to our knowledge is the first large-scale pan-Arab preference data set for the alignment of cultural behavior conditioned on the country. Our contribution includes two resources in English and Modern Standard Arabic: a culturally grounded preference dataset for training and evaluation, and a multiple-choice benchmark designed to assess culturally aligned behavior across nations. All test data are validated by native speakers to ensure authenticity. To optimize cultural alignment, we experiment with post-training methods across multilingual and Arabic-centric language models, evaluating performance on both generation tasks and our cultural multiple-choice benchmark. By releasing an evaluation benchmark, we provide a foundation for systematically studying and improving culturally aligned behavior in language models.

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  • arXiv cs.LGOct 18

    Operator-Mismatch Contrastive Learning for World-Relative Trajectory Coherence

    One Octadion, Firmansyah Sundana, Novanto Yudistira

    World models are increasingly judged by how well they rank plausible futures, yet plausibility alone is the wrong target. The same scene can evolve under many latent rules, and a future that is smooth and physically valid under one rule can be incoherent under the rule actually in force. We formalize the resulting failure, operator-blindness, and show that it is systematically hidden by standard evaluation: a scorer can attain near-perfect generic ranking while separating current-world from other-world futures no better than chance. We introduce Operator-Mismatch Contrastive (OMC) learning, whose semantic hard negatives are valid-but-wrong-operator trajectories, and a matching protocol that measures world-relative coherence directly. On a controlled synthetic benchmark with in- and out-of-distribution operator parameters, across two trajectory-scoring backbones, and on feature-level external validation with CoPhy ballsCF, OMC raises operator-sensitive scoring far above a chance baseline that nonetheless achieves 0.99 generic top-1. We prove that the wrong-operator negative is necessary both for the protocol to detect the failure and for the objective to fix it, and position OMC…

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  • arXiv cs.LGOct 18

    CD-OPD: Contextual Digest for On-Policy Distillation

    Mingyuan Li, Yucheng Li, Xiaxia Wang, Gong Cheng

    Teacher takeover in on-policy distillation (OPD) can produce incorrect responses even when the student and teacher independently answer the same problem correctly, a failure we quantify using Collaboration Failure Rate (CFR). To address it, we propose Contextual Digest for On-Policy Distillation (CD-OPD). CD-OPD structures the earlier context of teacher guidance through a teacher-generated dependency graph, curating the required problem conditions and preceding reasoning as node summaries alongside the unchanged guidance in a Contextual Digest before student continuation. We further develop online meta-reweighting, which compares Digest segment gradients with a task gradient from independent student responses to improve student-only performance, favoring updates predicted to reduce task loss. Across eight benchmarks, CD-OPD improves average accuracy by 1.32--7.05 percentage points over representative existing OPD methods. It also reduces CFR by 27.4\%--56.4\% relative to existing takeover-based OPD methods.

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  • arXiv cs.CVOct 18

    LateMatch: Breaking the Interaction Bottleneck via Cacheable Matching for Efficient Visual Localization

    Mohammad Omama, Aeyan Ashraf, Sandeep Chinchali

    Visual localization is critical for embodied agents, but deploying state-of-the-art deep feature matchers on edge devices is fundamentally limited by their massive computational cost. Standard deep matchers tightly interleave intra-image context building and inter-image matching at every layer, creating a severe interaction bottleneck. Localizing a single query against K database images forces the system to execute the full network K independent times with no capacity for caching. To break this bottleneck, we advocate for a paradigm shift toward cacheable image matching. We realize this paradigm with LateMatch, a versatile approach that restructures existing matchers by deferring inter-image interaction to their final layers. By isolating intra-image processing into offline-cacheable Context Blocks, only a small set of Interaction Blocks must be executed online. To further optimize LateMatch, we introduce E-SToRE (Entropy-guided Spatial Token Reduction and Expansion). E-SToRE leverages the decreasing Shannon entropy of the late Interaction Blocks to dynamically reduce token counts just prior to cross-image interaction. Evaluated across HPatches, ScanNet, MegaDepth, ETH-3D, and…

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  • arXiv cs.LGOct 18

    CHSM8: A Chess Transformer that Follows Natural-Language Style Prompts

    Ilia Badanin

    We present CHSM8, a 210M-parameter chess transformer that plays moves conditioned on a free-text description of a playing style, e.g. "go for the Sicilian Defense", "solid, safe, risk-free chess" or "You are Magnus Carlsen in his prime". The model is pretrained without search on 1.07B filtered Lichess games, using a factorized move representation and legal-move masking. It reaches 1837 Elo against a strength-limited Stockfish and, in 1,000-game matches, beat every other public model we tested that was likewise trained without preference tuning, rewards or search, including one with 13 times as many parameters. We then fine-tune it to follow text on a synthetic dataset: Gemma-4-31B describes the playing style of 3M game sides, a 1B Gemma-3 model distilled from the 2.4M of those descriptions that contradict nothing on the board labels the rest, and every one of 30M selected game sides gets five descriptions. Supervised fine-tuning attaches a frozen text encoder through gated cross-attention, and direct preference optimization (DPO) uses the same moves under a mismatched description as the rejected sample. After DPO, style adherence rises on every prompt we test (for example, queens…

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  • arXiv cs.CVOct 18

    Local Evidence, Global Failure: Diagnosing Speech-Vision Temporal Composition in Audio-Visual Language Models

    Jaehong Myung, Seunghun Oh, Unsang Park

    Audio-visual large language models (AV-LLMs) can fail at full-video speech-vision decisions even when useful visual evidence is locally recoverable. We introduce Temporal Evidence Decomposition (TED), a training-free diagnostic framework that separates local visual verification from speech-relative aggregation. Automatic speech recognition supplies a transcript and utterance interval. TED queries audio-free windows with the transcript for Temporal Alignment and Forecasting, or answer candidates for Cross-Modal Temporal Binding, then aggregates evidence relative to that interval. Across four frozen AV-LLMs on SVHalluc, Temporal Forecasting accuracy rises from 29.3-33.7% under full-video questioning to 79.3-82.2% with TED. Query replacement reduces recovery; the observed anchor remains beneficial when temporal-region availability is held fixed. Physical-isolation and timing-shortcut controls test alternative explanations. The findings distinguish locally recoverable evidence from successful full-video decisions, motivating explicit separation of narration content, utterance timing, and time-indexed visual evidence.

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  • arXiv cs.AIOct 18

    When Planning Problems Become Hard for LLMs: Separating Structural Complexity from Semantic Interpretability

    Mayukh Debnath, Adeyinka Adedigba, Rammohan Mallipeddi

    Large language models (LLMs) struggle with classical planning, but benchmarks rarely distinguish structural difficulty from the difficulty of interpreting symbolic vocabulary. We examine seven STRIPS domains with BFS-optimal plan lengths. For predicting structural difficulty without solving an instance, the zero-training delete-relaxation heuristic $h_{\mathrm{add}}$ outperforms learned alternatives on held-out domains (mean Spearman $|ρ|=0.931$), including ridge regression ($0.707$), object count ($0.657$), and the episodic retrieval architecture ARC ($0.400$). ARC also fails to outperform a linear model using the same features. To isolate semantic effects, we progressively rename predicates while preserving states, transitions, and optimal plans. Valid LLM planning success drops from $67.5\%$ to $40.0\%$ between $0\%$ and $100\%$ renaming, while refusals remain at $0\%$. An LLM supplied with ARC's conditioning information predicts difficulty in context better than trained ARC (mean $|ρ|=0.826$). Hybrid routing between an LLM and a symbolic planner depends more on calibration to the target LLM's outcomes than on the structural score selected. Thus, established symbolic…

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  • arXiv cs.LGOct 18

    Predicted Class Correction: Conformal Thresholds for Classes with No Calibration Data

    One Octadion, Novanto Yudistira, Yuta Nakashima

    Class-conditional conformal prediction calibrates a separate score threshold for every class and therefore requires labelled calibration examples of every class. In long-tailed deployments these are exactly what is missing: rare classes are left with few or no held-out examples, and existing methods are either undefined for them or fall back to the shared marginal threshold. We ask whether the per-class correction can instead be predicted from descriptors of the class that require no labels at all. We propose Predicted Class Correction (PCC), which regresses the offset between the per-class and the marginal conformal quantile on geometric descriptors of the classifier head, shrinks the prediction by a scalar selected on the labelled classes, and refits a single marginal offset. The descriptors depend only on the model's weights, so a class with zero calibration examples still receives its own threshold, and since only one scalar is calibrated, split conformal's marginal guarantee is retained. On ImageNet, PCC improves worst-class coverage on such classes by 0.058 [0.006, 0.110] at matched average set size, 42% of an oracle that sees the test labels. Of eight baselines on the same…

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  • arXiv cs.CVOct 18

    MCSplat: Multi-View Photometric and Geometric Consistent Feed-Forward Gaussian Splatting for Driving Scenes

    Yifei Wang, Wei Tian, Wenhua Bao, Jia Zhou, Hanshi Wang +8

    Driving-scene reconstruction via feed-forward Gaussian splatting from sparse multi-view images remains challenging due to two factors. First, cross-camera photometric inconsistency, caused by variations in exposure, ISP, and illumination, can be incorrectly absorbed into Gaussian geometry. Second, unreliable local geometric supervision arises from normal-based constraints near depth discontinuities. To address these issues, we present \textbf{MCSplat}, a multi-view consistent feed-forward Gaussian splatting framework for driving scenes. MCSplat constructs a canonical appearance space from depth-guided multi-camera correspondences to decouple geometry learning from camera-specific appearance. A multi-scale Bilateral Grid Transformer restores raw camera appearance from canonical renderings, enabling photometric supervision without contaminating Gaussian geometry. To improve local geometric reliability, we estimate Fisher-information-based uncertainty from photometric sensitivity and use it to adaptively weight surface-oriented self-supervision. Extensive experiments demonstrate improved reconstruction quality and generalization across driving datasets. Geometry-oriented evaluations…

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  • arXiv physics.comp-phOct 18

    FoundPDE: A generative foundation model for data-driven symbolic discovery of partial differential equations

    Langchen Liu, Junlin Luo, Min Zhu, Zhuoran Yang, Lu Lu

    Discovering governing partial differential equations (PDEs) from observed data is a central inverse problem in computational science and engineering: the goal is not only to predict trajectories, but to identify interpretable symbolic laws that can be analyzed and reused. Existing PDE discovery methods often depend on hand-designed libraries, accurate numerical derivatives, or per-equation symbolic search, making them fragile under sparse observations, noisy data, broad equation families, and unseen symbolic compositions. Here, we introduce FoundPDE, a pretrained data-to-symbol foundation model for PDE discovery. FoundPDE maps discretized spatio-temporal trajectories to symbolic equations with a transformer, uses Fourier and first-order spatial-derivative augmentation to expose complementary trajectory information, refines numerical coefficients, and supports downstream extension with improved generalization through few-shot fine-tuning and hierarchical discovery. On a clean in-domain benchmark spanning 20 PDE families, our model reaches 97.50% type recognition. Under coefficient shifts beyond the training support, type recognition remains 90.53%; under multiplicative Gaussian…

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  • arXiv cs.CVOct 18

    CASE: Complementary Articulation-Aware Speech Encoder to Enhance Speech-Driven 3D Facial Animation

    Jaehong Myung, Seungho Eum, Taegon Kim, Hyunjung Chung, Unsang Park

    General-purpose self-supervised speech encoders provide strong acoustic representations, but their pretraining objectives are not explicitly aligned with the visible articulatory dynamics required by speech-driven 3D facial animation. We propose CASE (Complementary Articulation-aware Speech Encoder), a complementary speech encoder adapted by contrastively aligning short speech clips with continuous 3D lip-mesh VQ-VAE latents. After adaptation, the geometry branch is removed, and the audio-only CASE representation is fused with the baseline SSL speech representation through residual cross-attention, while retaining the motion-decoder architecture and objective. Across four VOCASET baselines, CASE reduces mean lip vertex error by 2.8-6.0%, and mean full-face vertex error decreases in every system. Under the same fusion protocol, CASE yields lower mean LVE than the unadapted AV-SE stream on all four generators, with model-dependent margins. Because adaptation uses Korean speech-geometry pairs and evaluation uses English VOCASET, the result is consistent with cross-language downstream transfer under a shared English-pretrained audio-visual prior.

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  • arXiv cs.CCOct 18

    Formally Verified Autoresearch for Analyzing the Expressivity of Multi-Head Attention

    Karthik Viswanathan, Plamen Dimitrov, Stefano Rocca, Sarayu Sirikonda

    Multi-head attention is a central component of the transformer architecture, yet how its expressivity depends on the number of heads remains poorly understood. We study a one-layer, attention-only transformer with additive positional embeddings and define the head complexity $H^\ast(f)$ of a Boolean function $f$ as the minimum number of attention heads required to represent it. We derive an exact linear-fractional normal form for head complexity, prove a general threshold-degree lower bound, and give an explicit ten-bit function for which this bound is strict. For symmetric Boolean functions, we independently show that head complexity equals the number of times the function changes value between consecutive Hamming-weight levels, even when positional embeddings are allowed. This agrees with the concurrent characterization of Hsu and Xu (arXiv:2608.04243) in a model without positional embeddings. The symmetric lower bound combines our general threshold-degree bound with the classical result of Aspnes et al. (1994). These results were discovered and formalized by a frontier model through conjecture generation, theorem proving, and machine verification, while human researchers asked…

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  • arXiv cs.CVOct 18

    Edge Deployable Lightweight Framework for Real-Time Driver Safety System with Integrated Lane Departure and Traffic Sign Recognition

    Hassaan Ali Qureshi, Danish Hussain, Ali Hassan, Aon Safdar, Anas Bin Aqeel +3

    Lane departure and traffic-sign negligence are leading contributors to road accidents, particularly in regions with non-standardized signage and limited annotated data. Existing Advanced Driver Assistance Systems (ADAS) solutions are often developed as disjointed submodules, are rarely validated for real-time operation, and struggle to balance accuracy with low-latency inference for embedded deployment. We address these gaps with a unified, edge-deployable ADAS framework that simultaneously performs lane-departure warning (LDW) and traffic-sign detection and recognition (TSD/TSR), and validate the proposed system on automotive-class devices. Our framework combines a lightweight, lane-segmentation-driven LDW, which includes a drivable-area fallback for degraded markings, with a two-stage TSD/TSR pipeline consisting of efficient sign localization followed by fine-grained classification, designed to preserve accuracy at embedded latency. To support region-specific deployment under data scarcity, we curate a 35-class local traffic-sign dataset and introduce a generalized augmentation and balancing profile, expanding the corpus from 359 images to 8,750 samples. On an NVIDIA Jetson…

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  • arXiv cs.LGOct 18

    A Distribution-Free Separation Between Learned and Fixed Features

    Daniel Barzilai

    The success of neural networks is largely attributed to their ability to learn useful representations from training data. However, known results that prove a computational advantage of learned features over fixed ones are restricted to specific input distributions. We settle the COLT open question of Feldman, Kamath, and Srebro (2026) by showing that this advantage can persist in the distribution-free setting. We construct a family of hypothesis classes that can be efficiently learned by a natural gradient-based algorithm on a two-layer ReLU network, but cannot be represented by linear predictors over any fixed feature map with polynomial dimension (and runtime). Specifically, for any sufficiently small error and any sufficiently large runtime budget R (depending on the error), there exists a hypothesis class that the neural network learns under any input distribution to the desired expected classification error in time at most R, while the dimension complexity (governing the dimension needed by linear predictors) is superpolynomial in R.

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Rising research topics

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  • Computer vision50+4900.0%
  • Evals105+3400.0%
  • Image & video generation36+1100.0%
  • Federated learning50+35.1%
  • Agents525+1.0%
  • Quantum machine learning240.0%
  • Character recognition OCR170.0%
  • Sensing communication150.0%