activity
20202026
most citedRevisiting the Superficial Alignment Hypothesis

1 citations · 2 across the 8 of their papers we have counts for

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5 papers · 1 filter

cs.LG2026

Predicting Privileged Information Effectiveness for On-Policy Self-Distillation

Kaijian Zou, Emiliano Penaloza, MohammadHossein Rezaei +5

On-policy self-distillation (OPSD) enables large language models (LLMs) to learn from privileged information (PI) without a separate teacher model. However, prior work largely trea…

cs.LG2026

SWE-INTERACT: Reimagining SWE Benchmarks as User-Driven Long-Horizon Coding Sessions

Mohit Raghavendra, Anisha Gunjal, Aakash Sabharwal +1

We introduce SWE-Interact, a new testbed for evaluating coding agents on multi-turn, interactive, user-driven software engineering tasks. Existing frontier SWE benchmarks typically…

cs.LG2026

SWE Atlas: Benchmarking Coding Agents Beyond Issue Resolution

Mohit Raghavendra, Soham Dan, Miguel Romero Calvo +12

We introduce SWE Atlas, a benchmark suite for coding agents spanning three professional software engineering workflows: Codebase Q&A (124 tasks), Test Writing (90 tasks), and Refac…

cs.CL2026

LHAW: Controllable Underspecification for Long-Horizon Tasks

George Pu, Michael S. Lee, Udari Madhushani Sehwag +6

Long-horizon workflow agents that operate effectively over extended periods are essential for truly autonomous systems. Their reliable execution critically depends on the ability t…

cs.LG2026

Agentic Rubrics as Contextual Verifiers for SWE Agents

Mohit Raghavendra, Anisha Gunjal, Bing Liu +1

Verification is critical for improving agents: it provides the reward signal for Reinforcement Learning and enables inference-time gains through Test-Time Scaling (TTS). Despite it…