The paper introduces ThinkTwice, a framework that uses sampling and selection to improve document‑level information extraction. Instead of relying on greedy decoding, the method generates multiple candidate templates and selects the best one via an unsupervised agreement‑based approach or a supervised reward model. It also proposes a rejection‑sampling technique to create silver training data for reasoning traces, and experimental results show that ThinkTwice consistently outperforms greedy baselines and the current supervised state‑of‑the‑art.
By Mikel Zubillaga, Oscar Sainz, Oier Lopez de Lacalle, Eneko Agirre
The paper investigates how persona prompting influences the language produced by two multimodal large language models—Qwen3‑VL and Gemma4—when describing urban scenes. Outputs are categorized into descriptive grounding (captions), perception tags, and interpretive framing (justifications). Results show captions largely converge across persona profiles, while justifications differ markedly, especially along economic status, political orientation, and personality dimensions, with economic status producing the greatest variation. Perception tags also reflect attribute similarities, and exploratory topic analysis indicates persona‑specific evaluative emphasis. Overall, persona prompting has a stronger effect on interpretive framing than on descriptive grounding.
By Neemias da Silva, Matt Ratto, Myriam Delgado, Rodrigo Minetto, Daniel Silver, Thiago H Silva
The paper argues that current embodied vision‑language planning benchmarks favor linguistic next‑token prediction over physically grounded next‑state reasoning, leading models to rely on language priors rather than true causal dependencies. To address this, the authors introduce Causal‑Plan‑Bench, a diagnostic suite covering four causal dimensions, and Causal‑Plan‑1M, a million‑scale corpus of explicit causal reasoning traces extracted from egocentric videos. Extensive experiments show that existing models perform poorly on these tasks, while a new model trained with a tailored recipe—Causal Planner based on Qwen3‑VL‑8B—achieves significant gains, demonstrating the feasibility of physically grounded causal reasoning.
By Zheng Lu, Mingqi Gao, Qinlei Xie, Wanqi Zhong, Hanwen Cui, Zirui Song, Lijie Wang, Chong Luo, Bei Liu, Yiming Li
The paper investigates the optimization landscape of reinforcement learning with verifiable rewards (RLVR) on algorithmic tasks such as iterated group and quasigroup multiplication. By mapping entropy‑regularized RLVR with myopic tabular policies to a spin‑glass energy model, the authors prove that for a broad class of models with uncorrelated inputs the landscape is benign—there are no local minima that can trap training. They identify practical obstacles like diffusive barriers and gradient‑estimation errors, show these can be mitigated by choosing an appropriate entropy regulator, and demonstrate that a transformer trained from scratch with only last‑token rewards can learn an algorithmic chain of thought for iterated non‑Abelian group multiplications.
By Noa Rubin, Zohar Ringel
The note provides a detailed proof of Astra’s lower bound for differentially private continual counting, building on recent work by Harrison and Leeman. It discusses earlier results, including a Ω(√{3}√{log(n)}) bound by Bairaktari and Larsen and their subsequent Ω(log^2(n)) bound for pure differential privacy. The authors aim to offer a more natural and accessible proof, hoping to aid further research in the area.
By Jalaj Upadhyay
The study introduces a latent‑process multinomial processing tree (MPT) model to analyze human reading and comprehension of garden‑path sentences across four reading paradigms (eye tracking, uni‑ and bidirectional self‑paced reading, Maze). The model separates the likelihood of an incorrect initial analysis, the cost of encountering an incompatible continuation, and the cost of syntactic reanalysis, yielding more realistic parameter estimates when inattentive trials are considered. Cross‑validation shows that this MPT model predicts human reading patterns and end‑of‑trial judgments better than a model relying solely on large‑language‑model (LLM) surprisal, and that incorporating surprisal as an additional predictor further improves fit.
By Dario Paape, Tal Linzen, Shravan Vasishth
The paper introduces ELCD, a latent conflict detector that verifies LLM outputs after generation to catch instruction conflicts that static input checks miss. ELCD builds a hidden-state representation from the final-token embedding and the mean-pooled response embedding, then trains a pairwise margin ranking objective to distinguish compliant from drifting responses. Experiments on five large language models show ELCD outperforms baselines, boosting PR-AUC for Llama‑2‑7B by ~30 percentage points and cutting FPR95 for Mistral‑7B to 2.67%.
By Mingyu Ma, Yuxin Wu, Jingbo Wang, Tianxiao Huang, Leixin Sun, Xiaochuan Shi
GeoNLI introduces a unified, modular pipeline that combines advanced SAM variants with multimodal large language models to perform satellite image captioning, visual question answering (VQA), and visual grounding. The EarthMind model achieves strong results on captioning and VQA, while multiple RemoteSAM-SAM and DiffuSAM pipelines are used for grounding, ultimately employing a majority‑voting ensemble across several models. The system reports 82% captioning accuracy, 83.32% VQA accuracy, and 64.94% grounding accuracy, demonstrating improved consistency over task‑specific approaches.
By Ashutosh Gandhe, Anupam Rawat, Geet Sethi, Kabir Nasiruddin, Madhav Kotecha, Panav Shah, Rakshit Sawarn, Soumitra Nayak
The paper introduces scalable Graph Transformers for classifying healthy versus tumor epithelial cells in whole-slide images of cutaneous squamous cell carcinoma. By constructing a full‑WSI cell graph and incorporating morphological, texture, and neighboring cell class features, the proposed SGFormer and DIFFormer models outperform traditional image‑based methods, achieving balanced accuracies above 85% on single‑WSI tests and 83.6% on multi‑WSI evaluations. The study demonstrates that preserving tissue‑level context through graph representations improves classification of morphologically similar cell types.
By Lucas Sanc\'er\'e, No\'emie Moreau, Katarzyna Bozek
The paper presents EvalAgent, an AI assistant that automates agent evaluation by encoding domain expertise into evaluation skills such as procedural instructions, reusable code, and dynamic API retrieval. EvalAgent constructs a trace-based pipeline that outputs metrics, executable code, and reports, and is evaluated using a new meta-evaluation framework and AgentEvalBench. Results show that EvalAgent improves the Eval@1 metric from 17.5% to 65% and receives 79.5% human expert preference, while ablation studies confirm the importance of evaluation skills.
By Kang Zhou, Sangmin Woo, Haibo Ding, Kiran Ramnath, Subramanian Chidambaram, Aosong Feng, Vinayak Arannil, Muhyun Kim, Ishan Singh, Darren Wang, Zhichao Xu, Megha Gandhi, Nirmal Prabhu, Soumya Smruti Mishra, Smeet Dhakecha, Vivek Singh, Gouri Pandeshwar, Lin Lee Cheong
PROVE is a black‑box hallucination detector for medical visual question answering that tailors its verification strategy to each question’s evidential structure. It classifies questions into three regimes, activates a subset of five operators per regime, and calibrates operator importance using deterministic question‑answer features to produce a risk score. On 8048 test samples across three medical VQA benchmarks and four state‑of‑the‑art vision‑language models, PROVE achieves an AUROC of 0.821, surpassing the best baseline by 0.159 with consistent improvements across all models and datasets.
By Keyang Zhou, Siyi Li, Zhongnan Shi, Qichao Ying, Wei Tang, Zhenxing Qian
The paper introduces Rift, a two‑stage system that reduces the computational load of vision‑language models on satellites by pruning image tiles that do not affect the answer and then applying elastic prefill to limit token usage. By exploiting answer‑invariant token redundancy, Rift cuts energy consumption by 78 % and latency by 69 % compared to exhaustive tiled inference, while boosting accuracy from 45 % to 73 % on LLaVA‑1.5 7B running on a Jetson AGX Orin.
By Ishani Janveja, Davis Zhang, Seoyul Oh, Deepak Vasisht
The paper introduces a hypothesis-driven simulation workflow that screens customer experience (CX) agents before deployment, using synthetic customers and simulated tool outputs to emulate multi-step interactions without accessing production backends. Applied to Nubank’s high-volume Card Delivery and Card Management chat-support agents, the simulation’s binary evaluator scores correlated strongly with production results, and simulation-guided iterations raised transactional net promoter score by 36.69 points in a live A/B test. Additionally, screening over 16,000 simulated conversations helped select a model that increased self‑service rate by 8.82 percentage points without harming net promoter score, demonstrating that simulation enables extensive model exploration safely.
By Edesio Alcoba, Kevin Rossell, Aman Gupta, Shao Tang, Jiwoo Hong, Pabel Carrillo-Mendoza, Wanderson Concei\c{c}\~ao Ferreira, Alvaro Tedeschi, Zayd Simjee, Shreya Rajpal, Bruno Finardi Hime, Christian Sousa, Luis Moneda, Herbert Fei, Daniel Silva, Rohan Ramanath
The paper introduces a search‑aware reinforcement learning framework for multi‑component query understanding in Roblox game search. It first uses teacher‑student supervised fine‑tuning to create a schema‑compliant policy, then applies reinforcement learning that optimizes each query‑understanding component with component‑specific rewards derived from live search engine interactions. Experiments show that this approach improves per‑component utility and overall search quality, raising NDCG@20 by 8.9 points over the supervised baseline and 3.5 points over a single end‑to‑end reward strategy.
By Nayoung Choi, Shengjian Chen, Xiaokai Wei, Wenzheng Zhang, Daiyao Yi, Rachit Pareek, Vincent Su, Michelle Gong, Jinho D. Choi
The paper introduces Speculative Evaluation, a method to reduce variance in evaluating stochastic large language models (LLMs) under a fixed rollout budget. It employs a Hierarchical Bayesian Neyman (HBN) policy that first runs a short uniform pilot, then pools task-level success counts via a hierarchical Bayesian model to compute posterior expectations of task-level sampling variances. Using these expectations, the method applies exact positive-integer Neyman allocation to allocate rollouts, and an asynchronous variant (HBN-async) speculatively executes continuations from partial pilot feedback to mitigate synchronization overhead. Across six checkpoints and 18 benchmark groups, Speculative Evaluation achieves 12.8%-33.6% lower variance compared to uniform allocation, outperforming empirical and independent Bayesian baselines, and demonstrates practical benefits in real-generation experiments.
By Qianli Shen, Xiang Li, Ruomeng Ding, Yanxi Chen, Daoyuan Chen, Yaliang Li
The paper investigates how multi‑step tool‑calling large language model agents can unintentionally create persistent billable state, allowing malicious or compromised tools to generate repeated charges without user credentials. It formalizes the persistent billable‑state boundary, identifies six denial‑of‑wallet attack vectors, and evaluates them with the DOW‑BENCH harness across six model families. The study shows significant cost amplification, demonstrates effective mitigation via deterministic history transformation and host‑side invariants, and highlights the scarcity of existing safeguards in real‑world repositories.
By Jinqian Zhang (Institute of Information Engineering, Chinese Academy of Sciences, School of Cyber Security, University of Chinese Academy of Sciences), Haojun Xia (Institute of Information Engineering, Chinese Academy of Sciences, School of Cyber Security, University of Chinese Academy of Sciences), Shujiang Wu (Beihang University), Jingkun Yue (State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China), Xia Zhang (Institute of Information Engineering, Chinese Academy of Sciences, School of Cyber Security, University of Chinese Academy of Sciences), Zhangpei Cheng (Institute of Information Engineering, Chinese Academy of Sciences, School of Cyber Security, University of Chinese Academy of Sciences), Bibo Tu (Institute of Information Engineering, Chinese Academy of Sciences, School of Cyber Security, University of Chinese Academy of Sciences)
The paper introduces Domain Recentering with Confidence Calibration (DRC), a training‑free technique that adapts CLIP to unlabeled target images by fitting a Gaussian mixture and subtracting a posterior‑weighted average of component means from each embedding. It further corrects residual class bias using a log‑prior adjustment based on confidence‑weighted predictions. DRC outperforms other methods, raising average accuracy on cross‑domain datasets by 4.13 and 5.07 points over zero‑shot CLIP for ViT‑B/16 and ResNet‑50, and maintains gains under ImageNet distribution shifts.
By Youngeun Seol, Jimin Shin, Heeseo Yoon, Uiwon Hwang
The paper investigates a new attack method called output‑prefix attacks on reasoning LLMs, where an attacker prepends a malicious text to the model’s output, thereby conditioning all subsequent tokens on that prefix. The study systematically isolates the scratchpad reasoning channel as a vulnerable vector and compares three attack types—reasoning‑only, output‑prefix‑only, and combined reasoning‑plus‑output‑prefix—across both exposed and hidden reasoning models. Experiments on three 2026‑era frontier models (Gemini 3 Flash Preview, DeepSeek V4 Flash, and Claude Haiku 4.5) show that reasoning alone is largely ineffective, but adding a trivial output prefix can raise attack success rates to as high as 99% for some models, with contextual prefixes outperforming static ones and susceptibility varying by model.
By Luk\'a\v{s} Br\r{u}na, Robert Bridges, Adam Ek
The paper introduces StructFlow-HPR, a structured pose‑conditioned flow matching framework that generates realistic 5G channel state information (CSI) paired with human pose data. It learns a continuous latent transport process from Gaussian noise to real CSI, preserving the receiver‑frequency topology via a reconstruction‑preserving autoencoder. A pose‑conditioned Transformer models the latent velocity field, enabling pose‑aligned CSI generation through ordinary differential equation sampling, which improves human pose recognition performance when data are scarce.
By Haojin Li, Anbang Zhang, Wai Ho Mow, Chenyuan Feng, Chen Sun, Haijun Zhang
The paper investigates how neural networks, particularly Transformers and recurrent models, learn to track group elements by predicting their running product. It finds that Transformers tend to recover quotient classes and that their accuracy can be predicted by the reciprocal of class size, while recurrent networks can capture both normal and non‑normal right‑coset partitions. The study links partial accuracy, learning stages, and internal state representations to the subgroup cosets the models learn to track.
By Zhiyu Zhang, Yupeng Li