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From the 2 of 41 linked papers with an AI index.

activity
20242026
collaborators

41 papers

cs.CL2026

Thinking Hard, Not Smart: Reasoning Models Fail to Ration Test-Time Compute Across Questions

Chenrui Fan, Yize Cheng, Ming Li +3

Reasoning language models increasingly use test-time compute to improve performance, but existing evaluations typically study this compute one question at a time. Yet when multiple…

cs.LG2026

Retrieval is Enough: Training-Free Interpretability with a Tool-Using Agent

Sriram Balasubramanian, Soheil Feizi

Interpretability methods for neural network activations span a wide cost spectrum, from cheap, training-free techniques (such as linear probes, PCA, SVD) to more expensive training…

cs.AI2026

Do Agent Optimizers Compound? A Continual-Learning Evaluation on Terminal-Bench 2.0

Wenxiao Wang, Priyatham Kattakinda, Soheil Feizi

The paper evaluates whether gains from agent-optimization methods compound over successive optimization phases in a continual‑learning setting, using hard tasks from Terminal‑Bench…

cs.CV2026

SpurLens: Automatic Detection of Spurious Cues in Multimodal LLMs

Parsa Hosseini, Sumit Nawathe, Mazda Moayeri +2

The paper introduces SpurLens, an automated pipeline that uses GPT-4 and open-set object detectors to find spurious visual cues in multimodal large language models, showing that th…

cs.CL2026

When is Your LLM Steerable?

Chenrui Fan, Yize Cheng, Ming Li +2

Activation steering offers a lightweight approach to control language models' behavior at inference time, but whether it succeeds or fails heavily depends on the prompt, concept, m…

cs.CL2026

Off-the-Shelf LLMs as Process Scorers: Training-Free Alternative to PRMs for Mathematical Reasoning

Atoosa Chegini, Soheil Feizi

Selecting the best response from multiple small-model samples using a stronger scorer is a simple inference-time strategy, but fails when the small model has already committed to i…