SAGE is a governed multi‑stage LLM pipeline that transforms enterprise guideline documents—containing narrative text, tables, and images—into structured artifacts. It uses a shared versioned rule store, schema‑validated contracts, and provenance tracking to validate, score, and reconcile extracted rules, automatically approving high‑confidence outputs while flagging uncertain items for human review. In a test on 120 documents, SAGE reduced processing time from days to 20–100 minutes and achieved a 96% success rate with only 3.2% hallucination.
By Mohammadreza Sediqin, Shivali Dalmia, Sumukha Thoppanahalli, Srinivasa Karthikeya Reddy Kovvuri, Abhishek Mukherji
The paper investigates whether large language models decide to gather safety-relevant evidence before acting. Using the SAFE benchmark, the authors evaluate models such as GPT‑5.5, o3, Claude Opus, and Claude Sonnet, finding distinct evidence‑acquisition strategies that vary with retrieval cost, severity, and presentation. Across models, expected‑value reasoning dominates Stage 1 rationales, and evidence framing can alter decisions near the inspection threshold while probability is often cited despite limited influence.
By Omer Tafveez
The paper investigates how Vision‑Language Models (VLMs) often report high confidence even after self‑correcting or arriving at wrong answers, a phenomenon the authors attribute to the verbalized confidence being largely independent of the model’s reasoning trajectory. By analyzing content variation, token masking, and hesitation markers, the authors demonstrate that confidence does not adequately reflect the actual reasoning process and that calibration training can sometimes worsen this disconnect. To address this blind spot, they introduce the Trajectory‑Grounding Score (TGS) in two forms—TGS‑self and TGS‑pair—and propose TGS‑Bench, a suite of 10 benchmarks that reveal divergences between conventional calibration metrics and trajectory‑grounded confidence.
By Jisoo Yang, Jaeho Han, Trung X. Pham, Junyeong Kim
The paper introduces EffiRAG, a graph-based retrieval‑augmented generation system that reduces the cost of building and querying a graph by using it only to locate relevant passages and generating answers from the original text. On the UltraDomain benchmark, EffiRAG outperforms LightRAG‑hybrid in 93 of 120 questions while cutting total system cost by 57 % (from USD 0.952 to USD 0.408). The study shows that graph‑based RAG can be both more accurate and cheaper, especially as the corpus grows, and recommends evaluating such systems on both answer quality and cost.
By Yuzhong Zhang, Haoyang Ma, Chao Peng, Lionel Briand, Boxi Yu, Jialun Cao
GuardEn is an executable safeguard framework that transforms complex safety policies into atomic propositions via Safety‑Rule Compilation and models their composition as executable code. At inference, Scene‑Grounded Execution uses visual scene graphs to instantiate these propositions, allowing rule‑grounded and interpretable safety reasoning. Experiments on SafetyVisionBench show GuardEn improves F1 by an average of 9.8 points over the strongest baseline.
By Jisoo Kim (Sungkyunkwan University), TaeYoon Kwack (Sungkyunkwan University), Jinwoo Jang (Sungkyunkwan University), Honguk Woo (Sungkyunkwan University)
The paper introduces Market Signal Injection (MSI), an attack that alters how market data is formatted or described—without changing its numerical values—to influence large language model (LLM) pricing agents. Experiments on nine open‑weight and three proprietary models in simulated duopoly and triopoly markets show that sentiment‑based formatting changes cause significant shifts in firm behavior, profits, and consumer surplus. The study also demonstrates that model susceptibility varies across families, that larger models are not always more robust, and that techniques such as input canonicalization and decision boundary anchoring can partially mitigate these attacks.
By Dohun Lee, Hyunwoo Park
The study reports that ChatGPT 5.2 successfully passed a Finnish-language Turing Test conducted in Finland, contrary to expectations that uneven representation of Finnish in training data would hinder performance. The authors attribute failures mainly to participants using colloquial Finnish cues to identify human authorship. They propose reinterpreting the Turing Test as a comparative method to assess an AI’s credible membership in a specific social world, highlighting its utility for probing the human‑machine boundary across domains.
By Otto Segersven, Pentti Henttonen
The paper investigates whether in-context learning (ICL) in large language model agents reflects genuine recursive reasoning or simply statistical extrapolation. By testing LLM agents in a public goods game with manipulated historical feedback, the authors compare decision quality to a rational expectations equilibrium benchmark. They find that disrupting historical patterns eliminates the benefits of longer context, especially in highly interdependent settings, indicating that ICL behavior aligns more with statistical extrapolation than strategic reasoning.
By Yu Liu, Wenwen Li, Yifan Dou, Guangnan Ye
The paper introduces a text-based, multi-agent version of the board game Clue to test multi-step deductive reasoning in large language models (LLMs). Six LLM-based agents (GPT‑4o‑mini and Gemini‑2.5‑Flash) play turn‑based games, and a tool‑augmented approach uses a structured possibility matrix to convert implicit game state into explicit remaining possibilities, thereby offloading memory and deductive constraints from the agents. The study compares this tool‑augmented method against a baseline to assess its impact on reasoning quality and task success in a strategic reasoning environment.
By Rebecca Ansell, Autumn Toney-Wails
The paper investigates whether knowledge editing truly erases original facts from language models. Using a linear trace probe, the authors find that after editing a fact in GPT‑2‑XL, the original object remains highly decodable from hidden states across three different editing methods, even when the model behaves correctly on edited prompts. This suggests that editing suppresses rather than removes the original association in representational space.
By Priyansh Srivastava, Romit Chatterjee
The paper introduces a five-task diagnostic experiment that separates perceptual and reasoning failures in multimodal large language models on physics and geometry benchmarks. It finds that misinterpreting diagrams hurts performance even on text-only solvable problems, and that accuracy improves when models receive human-authored captions. The study shows that correcting captions can recover many errors, revealing distinct reasoning bottlenecks that differ by domain, while a heavily pretrained model still underperforms and often truncates reasoning traces.
By Raj Jaiswal, Sree Krishna Uppalapati, Dhruvkumar Patel, Ria Khatoniar, Tanuja Ganu, Rajiv Ratn Shah
WARD is a runtime‑adaptive Vision Transformer framework designed for edge AI that combines channel‑wise subnetwork partitioning, reliability‑aware continual learning, and dynamic operating‑mode scheduling. It operates two physically isolated subnetworks across four modes—Full‑Precision, Low‑Power, High‑Reliability, and Adaptive—to balance computational cost and fault tolerance while maintaining uninterrupted inference. Implemented on a lightweight FPGA accelerator with minimal area overhead, WARD achieves a network‑level failure rate of 1.79% under high Bit Error Rates and supports rapid mode transitions within a few clock cycles.
By Mahdi Taheri, Pramit Kumar Bhaduri, Mohammad Masoumi, Ali Mahani
The paper introduces DISCERN, a two-tier protocol for certifying that updates to production models do not increase risk. It first uses unlabeled data to detect benign updates based on disagreement rates, then selectively labels only disagreements through an anytime-valid confidence sequence. The method achieves finite-sample validity with label-complexity bounds of order ρ²/ε², demonstrating significant label savings and strong empirical performance across 14,000+ audit streams.
By Vishnu Bindu Balachandran
The paper investigates whether domain-specific fine‑tuning benefits open‑ended scientific reasoning in astronomy. Using a curated 300‑question QA benchmark from 2017–2026 Olympiad‑style materials, the authors compare open‑weight, API‑served general‑purpose, multimodal, and astronomy‑specialized language models. Results show that strong general‑purpose models set the highest correctness baseline, but variations in metric agreement, judge sensitivity, benchmark composition, and modality suggest that domain specialization is task‑ and deployment‑dependent and that domain‑specific evaluation is crucial for scientific workflows.
By Vanessa Lama, Sanjay Das, Emily Herron, Yuan-Sen Ting, Tijmen de Haan, Junqi Yin, Tirthankar Ghosal, Feiyi Wang
The study investigates whether AI assistance leaves a temporal fingerprint in writing and programming tasks. By analyzing keystroke-level data from three corpora, the authors find that AI contributions appear in distinct bursts and that temporal patterns can almost perfectly distinguish wholesale delegation from authentic work, though ordinary collaboration remains hard to detect. The research suggests that process visibility could serve as a basis for academic integrity checks.
By Eduardo Davalos, Yike Zhang
The study investigates how the identity of a judge influences outcomes in large language model (LLM)-as-judge panels, using a fully crossed pairwise design across four open-weight families (Llama 3.1, Qwen 2.5, Gemma 2, and Yi 1.5) with 9,312 judgments. A common per‑family statistic was found to be strongly confounded with candidate quality, prompting the authors to develop a corrected estimator that isolates judge effects while holding candidate family constant. The corrected analysis reveals a consistent positive same‑family lift (3.4–8.4 percentage points) across all families, with a global effect size of 0.067 (95 % CI [0.053, 0.084]) and a permutation p = 0.0002, and demonstrates that judge‑side likelihood and panel composition significantly influence outcomes.
By David Ababio Awuni, Luke E. K. Achenie, Benjamin Tei Partey, Elvis Gyasi Owusu, Nii-Nai Derrick Sowah
EDCT-Bench is a benchmark that evaluates the faithfulness of Vision‑Language Models (VLMs) by using Explanation‑Driven Counterfactual Testing (EDCT). EDCT extracts visual concepts from a model’s natural language explanation, applies minimal verified edits to those concepts, and checks whether the model’s answer and explanation remain consistent with the edited image. The benchmark covers three domains—knowledge‑intensive VQA, safety‑critical driving, and 3D spatial reasoning—and reveals significant faithfulness gaps in current VLMs, while also showing that EDCT‑generated counterfactuals can improve training.
By Sihao Ding, Santosh Vasa, Aditi Ramadwar, Thomas Monninger
The paper examines three recent studies that highlight distinct reliability issues in large language models (LLMs). Each study points to a missing capability—whether a tool‑reliability representation, safe generation behavior, or a consistent‑reasoning function—yet all converge on the need for calibrated abstention. The authors argue that current benchmarks fail to reward abstention, preventing the development of this function, and propose four evaluation changes to address the gap.
By Srijith Ravikumar
The paper introduces XConf, an experiential confidence estimator that augments a language model’s current inference with a record of its past graded episodes. By recalling similar past tasks and reflecting on past outcomes, XConf generates confidence scores without accessing logits or updating weights, achieving superior discrimination and calibration across diverse benchmarks. The method demonstrates significant gains in selective prediction, improving success rates on agent tasks by up to 8.7 points.
By Caiqi Zhang, Xiaochen Zhu, Chengzu Li, Yulong Chen, Dharshan Kumaran, Nigel Collier
HINT-Plan is a new method that integrates human intention prediction into robot task planning by using Vision Language Models to infer high‑level human intentions from third‑person images. These intentions are converted into goal states and combined with hierarchical Scene Graphs to formulate joint task‑planning problems in context‑rich environments. In a photorealistic simulation, HINT-Plan achieved a 69.71% success rate, outperforming baselines by up to 35.29% and reducing functional conflicts.
By Yuchen Liu, Luigi Palmieri, Lujun Li, Radu State, Ilche Georgievski, Marco Aiello