most citedThe science and practice of proportionality in AI risk evaluations

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cs.LG2026

Broken Chains: The Cost of Incomplete Reasoning in LLMs

Ian Su, Gaurav Purushothaman, Jey Narayan +5

Reasoning-specialized models like OpenAI's 5.1 and DeepSeek-V3.2 allocate substantial inference compute to extended chain-of-thought (CoT) traces, yet reasoning tokens incur signif…

cs.LG2026

Mitigating Forgetting in Continual Learning with Selective Gradient Projection

Anika Singh, Aayush Dhaulakhandi, Varun Chopade +3

As neural networks are increasingly deployed in dynamic environments, they face the challenge of catastrophic forgetting, the tendency to overwrite previously learned knowledge whe…

cs.LG2026

A Few Bad Neurons: Isolating and Surgically Correcting Sycophancy

Claire O'Brien, Jessica Seto, Dristi Roy +6

Behavioral alignment in large language models (LLMs) is often achieved through broad fine-tuning, which can result in undesired side effects like distributional shift and low inter…

cs.LG2025

Peek-a-Boo Reasoning: Contrastive Region Masking in MLLMs

Isha Chaturvedi, Anjana Nair, Yushen Li +5

We introduce Contrastive Region Masking (CRM), a training free diagnostic that reveals how multimodal large language models (MLLMs) depend on specific visual regions at each step o…

cs.LG2025

Sumudu Neural Operator for ODEs and PDEs

Ben Zelenskiy, Saibilila Abudukelimu, George Flint +2

We introduce the Sumudu Neural Operator (SNO), a neural operator rooted in the properties of the Sumudu Transform. We leverage the relationship between the polynomial expansions of…

cs.LG2025

Inference-Time Chain-of-Thought Pruning with Latent Informativeness Signals

Sophie Li, Nicholas Huang, Nayan Saxena +4

Large language models (LLMs) improve reasoning accuracy when generating multiple candidate solutions at test time, but standard methods like Best-of-N (BoN) incur high computationa…

cs.LG2025

PALADIN: Self-Correcting Language Model Agents to Cure Tool-Failure Cases

Sri Vatsa Vuddanti, Aarav Shah, Satwik Kumar Chittiprolu +4

Tool-augmented language agents frequently fail in real-world deployment due to tool malfunctions--timeouts, API exceptions, or inconsistent outputs--triggering cascading reasoning…

cs.LG2025

Amortized Latent Steering: Low-Cost Alternative to Test-Time Optimization

Nathan Egbuna, Saatvik Gaur, Sunishchal Dev +2

Test-time optimization remains impractical at scale due to prohibitive inference costs--techniques like iterative refinement and multi-step verification can require …