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5 papers

cs.CL2026

PiCSAR: Probabilistic Confidence Selection And Ranking for Reasoning Chains

Joshua Ong Jun Leang, Zheng Zhao, Aryo Pradipta Gema +7

The paper proposes PiCSAR, a training-free scoring method that uses the joint log-likelihood of reasoning steps and final answer to select the most reliable reasoning chain from mu…

cs.CL2026

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…

cs.CV2026

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…

cs.CL2026

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…

cs.AI2026

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…