activity
20232026
most citedGEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning

2 citations · 4 across the 6 of their papers we have counts for

collaborators

7 papers

math.GT2026

Minimal Complexes of Rational Tangles via the Fukaya Category

Jonathan A. Higgins

We compute minimal complexes for oriented rational tangles, denoted , in terms of the topological Fukaya category of the -punctured disk…

cs.CL2026

optimize_anything: A Universal API for Optimizing any Text Parameter

Lakshya A Agrawal, Donghyun Lee, Shangyin Tan +11

Can a single LLM-based optimization system match specialized tools across fundamentally different domains? We show that when optimization problems are formulated as improving a tex…

cs.SE2026

Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces

Mike A. Merrill, Alexander G. Shaw, Nicholas Carlini +82

AI agents may soon become capable of autonomously completing valuable, long-horizon tasks in diverse domains. Current benchmarks either do not measure real-world tasks, or are not…

cs.CL2025★ 2 cited

GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning

Lakshya A Agrawal, Shangyin Tan, Dilara Soylu +14

Large language models (LLMs) are increasingly adapted to downstream tasks via reinforcement learning (RL) methods like Group Relative Policy Optimization (GRPO), which often requir…

cs.CL2025

LangProBe: a Language Programs Benchmark

Shangyin Tan, Lakshya A Agrawal, Arnav Singhvi +6

Composing language models (LMs) into multi-step language programs and automatically optimizing their modular prompts is now a mainstream paradigm for building AI systems, but the t…

cs.LG2025

S*: Test Time Scaling for Code Generation

Dacheng Li, Shiyi Cao, Chengkun Cao +6

Increasing test-time compute for LLMs shows promise across domains but remains underexplored in code generation, despite extensive study in math. In this paper, we propose S*, the…

cs.CL2023★ 2 cited

DSPy Assertions: Computational Constraints for Self-Refining Language Model Pipelines

Arnav Singhvi, Manish Shetty, Shangyin Tan +4

Chaining language model (LM) calls as composable modules is fueling a new way of programming, but ensuring LMs adhere to important constraints requires heuristic "prompt engineerin…