The paper investigates whether large language model–based coding agents can automatically synthesize programs that solve generalized task and motion planning (TAMP) problems across diverse instances. Using Claude Code and Codex, the authors evaluate 980 generated programs on 100 held‑out environments from KinDER and PDDLStream, achieving mean success rates between 56 % and 95 %—higher than hand‑engineered planners and other baselines—while requiring an order of magnitude less computation per instance. The study demonstrates that coding agents can calibrate physical models, test edge cases, and refine strategies, suggesting they are a strong baseline for generalized TAMP.
By Matteo Merler, Bowen Li, Josh Roy, Yichao Liang, Qianwei Wang, Yixuan Huang, Tom Silver
SheetMind is a Manager‑Action‑Reflection framework that evaluates how much spreadsheet agent performance derives from the agents themselves versus the shared action interface. In a controlled study on all 221 tasks of the SheetCopilot Benchmark, replacing the high‑level action API with primitive cell operations drops accuracy by 47.1 points, while adding a Reflection Agent improves performance by 4.5 points and a Manager by 1.4 points. The framework also shows that decomposition changes failure modes, reducing silent wrong outputs from 33% to 25%, and that GPT‑5 and GPT‑5‑mini achieve similar performance, whereas GPT‑3.5 underperforms significantly.
By Lyuhao Chen, Xi Cheng, Yanming Kang, Ruiyan Zhu, Ke Liu, Rakesh Chowdary Machineni, Yulang Fei, Brian Zhu, Daniel Jin, Binze Cai, Zheng Qi, Neeraj Parihar, Zhoutian Xu, Oliver Gao
SPARQL-LLM is an open‑source, triplestore‑agnostic system that generates SPARQL queries from natural language using lightweight metadata and dedicated components for indexing, prompt building, and execution. It achieves up to 59 % higher F1 scores than the next best system on a multilingual challenge and on bioinformatics knowledge graphs, while being up to 27 × faster and costing no more than $0.01 per question. The project is publicly available on GitHub and is already deployed on real‑world decentralized knowledge graphs such as expasy.org/chat.
By Panayiotis Smeros, Vincent Emonet, Ruijie Wang, Ana-Claudia Sima, Tarcisio Mendes de Farias
The paper proposes that two architectural assumptions—(1) attention and MLPs share a key‑value form <phi(S)>U, and (2) components read from an additive residual stream—are sufficient to answer three interpretability questions: component interaction, information routing, and token attribution. By treating these selections as a computational graph, the authors develop Unpack, a backward attribution method that validates interaction scores, recovered routes, and token attribution against established tests across models ranging from 160M to 6.9B parameters. The study also shows that contribution and causal effect can differ, with a recognizable signature in how components change when a task is removed.
By Po-Kai Chen, Aske Plaat, Niki van Stein
The paper introduces a neuro‑symbolic architecture that combines symbolic planning, reinforcement learning, and a large language model (LLM) to address novelties in dynamic open‑world environments. The LLM is used to identify missing operators, generate symbolic plans, and write reward functions, enabling the reinforcement learning agent to learn control policies for newly identified operators. The proposed method outperforms state‑of‑the‑art approaches in both operator discovery and learning within continuous robotic domains.
By Hong Lu, Pierrick Lorang, Timothy R. Duggan, Jivko Sinapov, Matthias Scheutz
Task-Aware Spectral Pruning (TASP) is a post‑training framework that tailors sparse masks to specific tasks by calibrating module‑level spectral descriptors against task‑specific ablation effects. It constructs masks that close grouped‑query‑attention and SwiGLU dependencies, routing each user turn to a single compiled mask that remains fixed during prefill and decoding. In experiments, TASP achieves a 43% active‑FLOP reduction while preserving 97.7% of the dense BF16 performance on Llama‑3‑70B, and delivers a 1.44× speedup on an A100 80GB with INT8‑weight/BF16‑compute, reducing decode latency from 45.2 to 31.3 ms/token.
By Ibne Farabi Shihab, Fariya Afrin, Sanjeda Akter, Anuj Sharma
UltraBench 2 is a new benchmark designed to evaluate vision foundation models on ultrasound images, addressing the lack of standardized tests in this area. It covers a wide range of anatomical structures and tasks, emphasizing reproducibility and ease of use. The authors compare existing models, finding that ultrasound-specific pretraining still outperforms on classification, while general-purpose models have matched performance on segmentation.
By Ashwath Radhachandran, Adam Tupper, Christian Gagn\'e, William Speier
The paper introduces COSA-GS, a new compression method for 3D Gaussian Splatting that avoids spatial aggregation by using anchor-wise causal factorization. It builds a compact learnable anchor latent from geometry context and fuses it with the geometry context to create an anchor context for attribute coding, employing only linear transformations and activations. The method is trained with rate–distortion optimization, adaptive Gaussian pruning, and quantization-aware training to ensure bit‑exact entropy decoding across platforms, achieving state‑of‑the‑art compression performance with fast, consistent cross‑platform decoding.
By Pengpeng Yu, Yueru Chen, Fei Song, Tai Qin, Qi Zhang, Jing Wang, Yulan Guo
The paper evaluates two large language models, Claude Sonnet 4.5 and Claude Opus 5, on the bidirectional English Resource Grammar (ERG) tasks of generating English from Minimal Recursion Semantics (MRS) and parsing English into MRS. In generation, Opus achieves 76.3 BLEU—surpassing a 72k‑pair trained system and matching a million‑pair system—while Sonnet scores 65.7 BLEU, rising to 69.6 when selecting from ACE’s candidates. In parsing, both models lag behind ACE, attaining only 57.2 and 65.5 F₁ respectively, with exact‑match on about 1 % of sentences, highlighting that high generation scores do not guarantee accurate semantic parsing.
By Soham Dan
The paper introduces MARETopic, a training‑free framework that identifies topics by selecting rank‑based prototype documents from pretrained embeddings. By projecting embeddings onto a low‑dimensional manifold and building ranked neighborhood lists, a greedy algorithm picks exactly K exemplar texts whose neighborhoods cover the corpus. Two variants—MARETopic_Corr, which uses a query‑performance predictor and rank correlation, and MARETopic_Diff, which employs a rank‑based diffusion matrix—achieve higher purity and NMI on benchmark datasets and run significantly faster, while also improving topic coherence and vocabulary diversity through a novel Maximal Marginal Relevance step.
By Thiago C\'esar Castilho Almeida, Daniel Carlos Guimar\~aes Pedronette
Cross-Modal Emotion Understanding: A Transformer-GAT Approach for Dialogue Emotion Recognition proposes a hybrid framework that combines a Transformer and a Graph Attention Network to capture both global semantic information and fine-grained relationships between modalities. The model is evaluated on the IEMOCAP and MELD datasets, achieving weighted F1 scores of 72.45% and 77.37%, respectively, and surpasses state‑of‑the‑art methods. These results suggest that integrating multimodal features with balanced global and local context modeling can provide deeper emotional insights for dialogue emotion recognition.
By Jiaqi Qiao, Yifan Lyu, Xiujuan Xu
A useful visual representation is a statistic of the observed past that retains the latent factors shared with the future and discards patch-private noise. We formalize this requirement as a condition...
As electric vehicle (EV) adoption increases, ensuring efficient and well-distributed charging infrastructure has become a critical challenge. While many EV charging station location problem (CSLP) stu...
OmniFabric is a new method for creating high‑quality, globally coherent texture maps for 3D garment reconstruction from a single image. It first generates a coarse texture initialization on the garment’s sewing pattern using a 3D mesh and Vision‑Language Model priors, then refines this in the UV domain with a diffusion transformer conditioned on 3D positional features. The approach removes distortion and baked‑in artifacts, producing photorealistic 3D garments that outperform existing baselines.
The paper investigates whether coding agents can automate the synthesis of programs that solve generalized Task and Motion Planning (TAMP) problems. By evaluating Claude Code and Codex on 28 simulated environments, the authors find that these agents outperform hand-engineered planners and other baselines, achieving higher success rates and lower computation per instance. The agents also demonstrate adaptive behaviors such as calibrating physical models and refining strategies during interaction.
The paper introduces an acceptance protocol for sensor-coordinate and polarity binding in mechatronic commissioning, separating candidate generation from release authority. It routes unsupported requirements to a frozen four‑billion‑parameter local language model and only releases plans when both facts can be derived by an external gate under a sealed grammar, with a gold‑standard user providing a canonical answer when eligible. Evaluation on 144 tasks showed that fabricated plans were correctly rejected, no false releases occurred among 83 releases, and protection against incorrect user answers was characterized across 431 pairings.
The paper introduces BRIE, a scalable framework that automatically creates question–answer pairs from longitudinal electronic health record notes, validated by nineteen clinicians. It offers a continuously maintainable benchmark for evaluating large language models in clinical settings, addressing limitations of manual, costly, and quickly outdated existing benchmarks. Experiments across nine LLMs and five inference strategies reveal that even state‑of‑the‑art systems often miss clinically important information, especially for synthesis‑heavy queries.
ExplorationBench is a benchmark designed to evaluate AI systems’ ability to conduct scientific exploration in verifiable alien worlds, where rules are executable and can be precisely checked. It consists of two sandboxes—AlienCode and AlienLogic—each offering discovery targets, tasks, flawed manuals, environmental feedback, and tool‑call schemas. The benchmark tests whether systems can generate new hypotheses, design experiments, and iterate on results, rather than merely recalling pre‑trained knowledge, and finds that top performers can acquire and apply unfamiliar rules, though performance varies across exploration trajectories.
GRASP is a multi-stage planning framework that separates planning into specialized modules: GenPlan for global macro-guidelines, RevPlan for exploring localized strategies, and VerPlan for multi-criteria evaluation. This strategy-aware approach yields state‑of‑the‑art accuracy on diverse datasets, outperforming direct LLM planners by up to 30.8% on ZebraLogic and reducing multi‑task degradation. GRASP’s context isolation and macro‑regularization also give it a 14.5% edge over frontier reasoning models like GPT‑5‑mini.
The Era by Eon benchmark tests enterprise agents by presenting questions that specify answer rules and require code to compute answers from a company’s data. In the original benchmark, top models answered 22–25 of 27 questions, barely distinguishing performance. The updated benchmark adds eight templates that rely on hidden facts not explicitly stated in any question or document, forcing agents to infer information from indirect data. Twelve agents were evaluated, with the best achieving 18 of 24 correct answers, while the hardest questions—requiring selection among similar records—were answered correctly only 1 out of 84 attempts across all agents.