Papers with pass rate
TDFlow: Agentic Workflows for Test Driven Development (2026.eacl-long)
Copied to clipboard
| Challenge: | TDFlow decomposes software engineering program repair into four components governed by sub-agents. |
| Approach: | They propose a test-driven agentic workflow that frames repository-scale software engineering as a testing task and decomposes it into four components governed by sub-agents. |
| Outcome: | The proposed workflow achieves 88.8% pass rate on SWE-Bench Lite and 94.3% on Swe-Bech Verified when provided with human-written tests. |
RLMEval: Evaluating Research-Level Neural Theorem Proving (2025.findings-emnlp)
Copied to clipboard
| Challenge: | RLMEval evaluates large language models for research-level neural theorem proving and proof autoformalization . the best model achieves only a 10.3% pass rate on existing benchmarks . |
| Approach: | They propose a new evaluation suite for large language models . it evaluates research-level theorems from real-world Lean formalization projects . |
| Outcome: | RLMEval evaluates research-level theorems from real-world Lean formalization projects. |
RETAIL: Towards Real-world Travel Planning for Large Language Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing travel planning systems assume users provide explicit queries, limiting their practical utility. |
| Approach: | They propose a dataset RETAIL which supports decision-making for implicit queries while covering explicit queries. |
| Outcome: | The proposed model achieves a 1.0% pass rate, suggesting real-world travel planning remains challenging. |
TRUEBench: Can LLM Response Meet Real-world Constraints as Productivity Assistant? (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing benchmarks fail to evaluate large language models' instruction-following capabilities . current benchmarks lack multilinguality, implicit constraints and multi-turn dialogue . |
| Approach: | a new benchmark is designed to evaluate large language models' instruction-following capabilities . the benchmark features input prompts across 12 languages and includes inter-instance multilingual instructions . |
| Outcome: | a new benchmark for large language models (LLMs) is designed to assess their performance in real-world settings. |
ToolGrad: Efficient Tool-use Dataset Generation with Textual “Gradients” (2026.findings-acl)
Copied to clipboard
| Challenge: | Prior work synthesizes tool-use LLM datasets by first generating a user query, then complex tool-using annotations like DFS. |
| Approach: | They propose an agentic framework that synthesizes user queries and generates valid tool-use chains . they propose a dataset with more complex tool use, lower cost, and almost 100% pass rate . |
| Outcome: | Experiments show that tools trained on ToolGrad outperform expensive baseline datasets and proprietary LLMs. |
IoTMigrator: LLM-driven Embedded IoT Code Migration across Different OSes for Cloud-device Integration (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Neither outline-based code generation nor common code translation techniques can adequately address this challenge, despite their prevalence in existing systems. |
| Approach: | They have developed an algorithm that employs a multi-agent pipeline to handle embedded code migration under the TSL paradigm. |
| Outcome: | The proposed algorithm outperforms the baseline by 50.5% for pass rate and 13.0% for completeness across all tasks in RIOT and Zephyr. |
Tool learning via Inference-time Scaling and Cycle Verifier (2025.findings-acl)
Copied to clipboard
| Challenge: | In inference-time scaling, Chain-of-Thought (CoT) data is scarce or even unavailable. |
| Approach: | They propose a method which establishes an inference cycle to synthesize user queries and CoT data. |
| Outcome: | The proposed method achieves a 75.4% pass rate and a 79.6% win rate using small models in StableToolBench. |
Programming by Example meets Historical Linguistics: A Large Language Model Based Approach to Sound Law Induction (2025.acl-long)
Copied to clipboard
Atharva Naik, Darsh Agrawal, Hong Sng, Clayton Marr, Kexun Zhang, Nathaniel Romney Robinson, Kalvin Chang, Rebecca Byrnes, Aravind Mysore, Carolyn Rose, David R. Mortensen
| Challenge: | Historical linguists have written programs that convert reconstructed words into their attested descendants via ordered string rewrite functions. |
| Approach: | They propose to use a model to generate a "similar distribution" for sound law induction . they propose four kinds of methods with varying amounts of inductive bias to investigate best performance . |
| Outcome: | The proposed model shows that it can be fine tuned with training data and evaluation data. |
Reasoning While Asking: Transforming Reasoning Large Language Models from Passive Solvers to Proactive Inquirers (2026.acl-long)
Copied to clipboard
| Challenge: | Existing reasoning-oriented LLMs lack a blind self-thinking paradigm . current models fail to recognize when their reasoning is underinformed or based on ambiguous user instructions . |
| Approach: | They propose a new reasoning paradigm that transforms LLMs into proactive inquirers that interleave reasoning with clarification. |
| Outcome: | The proposed model outperforms baseline models on mathematical reasoning, code generation, and document editing. |
Audio MultiChallenge: A Multi-Turn Evaluation of Spoken Dialogue Systems on Natural Human Interaction (2026.acl-long)
Copied to clipboard
Advait Gosai, Tyler Vuong, Utkarsh Tyagi, Steven Li, Wenjia You, Miheer Bavare, Arda Uçar, Zhongwang Fang, Brian Jang, Bing Liu, Yunzhong He
| Challenge: | End-to-end (E2E) spoken dialogue systems are replacing cascaded pipelines for voice-based human-AI interaction. Existing benchmarks evaluate these systems on synthetic speech and single-turn tasks, leaving multi-turn conversational ability underexplored. |
| Approach: | They propose an open-source benchmark to evaluate spoken dialogue systems under natural multi-turn interaction patterns. |
| Outcome: | The proposed model fails on the highest-performing model with 54.65% pass rate. |
CAR-bench: Evaluating the Consistency and Limit-Awareness of LLM Agents under Real-World Uncertainty (2026.acl-long)
Copied to clipboard
| Challenge: | Existing benchmarks for Large Language Model (LLM) agents focus on task completion under idealistic settings but overlook reliability in real-world, user-facing applications. |
| Approach: | They propose a benchmark to evaluate consistency, uncertainty handling, and capability awareness in multi-turn, tool-using LLM agents in an in-car assistant domain. |
| Outcome: | The proposed benchmarks evaluate consistency, uncertainty handling, and capability awareness in multi-turn, tool-using LLM agents in an in-car assistant domain. |