6 papers
Chain-of-Thought Faithfulness of Reasoning Models Varies with Where and How Preference Cues Are Delivered
Aryo Pradipta Gema, Neel Rajani, Rohit Saxena +2
Chain-of-thought (CoT) monitoring assumes that reasoning traces faithfully record the information that shapes a model's answer. Existing faithfulness tests often place explicit bia…
Do Composed Image Retrieval Benchmarks Require Multimodal Composition?
Matteo Attimonelli, Alessandro De Bellis, Aryo Pradipta Gema +8
Composed Image Retrieval (CIR) is a multimodal retrieval task where a query consists of a reference image and a textual modification, and the goal is to retrieve a target image sat…
Analyzing LLM Instruction Optimization for Tabular Fact Verification
Xiaotang Du, Giwon Hong, Wai-Chung Kwan +4
Instruction optimization provides a lightweight, model-agnostic approach to enhancing the reasoning performance of large language models (LLMs). This paper presents the first syste…
Same Answer, Different Representations: Hidden instability in VLMs
Farooq Ahmad Wani, Alessandro Suglia, Rohit Saxena +6
The robustness of Vision Language Models (VLMs) is commonly assessed through output-level invariance, implicitly assuming that stable predictions reflect stable multimodal processi…
OpenSIR: Open-Ended Self-Improving Reasoner
Wai-Chung Kwan, Joshua Ong Jun Leang, Pavlos Vougiouklis +3
Recent advances in large language model (LLM) reasoning through reinforcement learning rely on annotated datasets for verifiable rewards, which may limit models' ability to surpass…
PiCSAR: Probabilistic Confidence Selection And Ranking for Reasoning Chains
Joshua Ong Jun Leang, Zheng Zhao, Aryo Pradipta Gema +7
Best-of-n sampling improves the accuracy of large language models (LLMs) and large reasoning models (LRMs) by generating multiple candidate solutions and selecting the one with the…