The Missing Complement: State-Conditioned Minimal Sufficient Evidence for Coding Agents
MSS-Complement method recovers state-conditioned minimal sufficient evidence for coding agents, achieving 73.0% coverage at five items.
Key Findings
Methodology
This paper introduces the MSS-Complement method for recovering minimal sufficient evidence needed by coding agents during decision-making. The method constructs a jointly sufficient set through semantic calls, searches for missing parts, and returns 4-8 intact source units within 6,144 tokens.
Key Results
- MSS-Complement achieves 73.0% coverage at five items and 80.6% at eight, significantly outperforming Qwen3 embedding with reranking at 61.4% and 72.4%.
- On AMA-Bench, MSS-Complement answers from a 76.2% smaller prompt with 2.08 points higher accuracy.
- In Action52, removing one required group results in a 12.3 and 11.1 point drop in repair-localization precision.
Significance
This study significantly enhances evidence recovery for coding agents through the MSS-Complement method. It improves evidence coverage and reduces unnecessary information redundancy, advancing the field of information retrieval.
Technical Contribution
MSS-Complement employs a set construction strategy rather than ranking for evidence acquisition, offering a more efficient recovery mechanism compared to existing methods. It ensures decision sufficiency through semantic calls and evidence joint checks.
Novelty
MSS-Complement is the first to treat evidence acquisition as set construction rather than ranking, significantly enhancing decision support for coding agents with higher evidence coverage.
Limitations
- The method may struggle with complex, diverse evidence needs, especially with a large number of evidence groups.
- Further validation is needed across different tasks and datasets to confirm its generalizability.
Future Work
Future research could explore the application of MSS-Complement in other fields like medical diagnosis and legal analysis, and optimize its performance across different tasks.
AI Executive Summary
Coding agents often face challenges of insufficient evidence when solving problems, and traditional ranking methods fail to effectively support decision-making. The MSS-Complement method offers a new solution through state-conditioned minimal sufficient evidence recovery. This method constructs a jointly sufficient set through semantic calls, searches for missing parts, and returns 4-8 intact source units within 6,144 tokens, significantly improving evidence coverage.
Experimental results show that MSS-Complement achieves 73.0% coverage at five items and 80.6% at eight, significantly outperforming Qwen3 embedding with reranking at 61.4% and 72.4%. On AMA-Bench, the method answers from a 76.2% smaller prompt with 2.08 points higher accuracy. Additionally, in Action52, removing one required group results in a 12.3 and 11.1 point drop in repair-localization precision.
This research not only makes breakthroughs in evidence recovery for coding agents but also provides new insights for the field of information retrieval. Future research could explore its application in other fields and optimize its performance across different tasks. However, the method still has limitations in handling complex, diverse evidence needs and requires further validation for its generalizability.
Deep Analysis
Background
Coding agents play a crucial role in software development, but they often face challenges of insufficient evidence when dealing with complex problems. Traditional information retrieval methods typically rely on ranking mechanisms, which struggle to meet diverse evidence needs. Recently, researchers have begun exploring more efficient evidence acquisition methods to enhance decision support for coding agents.
Core Problem
Coding agents require sufficient evidence support during decision-making, but traditional ranking methods often fall short. Especially when faced with complex evidence needs, a single ranking mechanism cannot effectively cover all necessary information, leading to insufficient decision support.
Innovation
The MSS-Complement method employs a set construction strategy rather than ranking, significantly enhancing evidence recovery efficiency. It constructs a jointly sufficient set through semantic calls, ensuring decision sufficiency and optimizing evidence coverage by searching for missing parts.
Methodology
- �� Construct a jointly sufficient set through semantic calls
- �� Search for missing evidence parts
- �� Return 4-8 intact source units within 6,144 tokens
- �� Ensure decision sufficiency
Experiments
Experiments were conducted on 500 states across 45 repositories using SERBench for evaluation. MSS-Complement was compared with Qwen3 embedding and reranking methods, showing significant advantages in evidence coverage.
Results
MSS-Complement achieves 73.0% coverage at five items and 80.6% at eight. On AMA-Bench, it answers from a 76.2% smaller prompt with 2.08 points higher accuracy.
Applications
MSS-Complement can be applied in software development for coding agents, helping them acquire sufficient evidence support in complex problems, enhancing decision efficiency.
Limitations & Outlook
Despite its impressive performance in evidence recovery, MSS-Complement still faces limitations in handling complex, diverse evidence needs and requires further validation for its generalizability.
Plain Language Accessible to non-experts
Imagine you're cooking in a kitchen, and you need all the ingredients to make a dish. Traditional methods are like sorting all ingredients by importance, but this doesn't guarantee you have enough to complete the dish. The MSS-Complement method is like a smart assistant that helps you find all necessary ingredients and ensures you have enough to complete the dish. This way, you can cook more efficiently without worrying about missing a crucial ingredient.
ELI14 Explained like you're 14
Imagine you're playing a game where you need to collect specific items to pass a level. Traditional methods are like sorting items by importance, but this doesn't guarantee you have all the necessary items. The MSS-Complement is like a smart assistant that helps you find all the required items and ensures you have enough to pass the level. This way, you can finish the game faster without worrying about missing a key item.
Glossary
MSS-Complement
A method for recovering minimal sufficient evidence needed by coding agents during decision-making.
Used in this paper to improve evidence coverage.
SERBench
A benchmark for evaluating evidence recovery capabilities of coding agents.
Used in experiments to test MSS-Complement's performance.
Qwen3 Embedding
An embedding method for information retrieval.
Used as a comparison method.
Semantic Calls
Steps used to construct a jointly sufficient set.
A core step in the MSS-Complement method.
Evidence Coverage
Refers to the completeness of evidence needed for decision-making.
Used to evaluate the effectiveness of MSS-Complement.
Open Questions Unanswered questions from this research
- 1 How can MSS-Complement's effectiveness be validated on larger datasets? Current experiments are limited in scale and need further expansion.
- 2 Can MSS-Complement be applied to other fields like medical or legal analysis? Its cross-domain applicability needs exploration.
Applications
Immediate Applications
Software Development
Helps coding agents acquire sufficient evidence support in complex problems, enhancing decision efficiency.
Long-term Vision
Cross-Domain Applications
Explore MSS-Complement's potential in fields like medicine and law, driving cross-domain technological advancements.
Abstract
A coding agent halfway through an issue has already read much of what a retriever ranks highest. Relevance is scored per passage, but sufficiency belongs to the set: a ranker can fill its budget with variants of one required fact and leave the decision unsupported. We formulate state-conditioned minimal sufficient evidence recovery: given a captured agent state, recover a compact evidence combination that supplies the support its next decision still lacks. SERBench measures this on 500 held-out states from 45 repositories, recording what the agent has seen and crediting only sets that cover every fact the current decision was annotated to require. MSS-Complement treats acquisition as set construction, not ranking. Three semantic calls propose a jointly sufficient set, search for what it lacks, and return 4-8 intact source units within 6,144 tokens. One configuration, fixed on calibration data, recovers a complete set for 73.0% of those states at five items and 80.6% at eight, against 61.4% and 72.4% for Qwen3 embedding with reranking. A matched control ranking by similarity alone reaches 66.6%, placing the gain in the set-level policy, not the computation. From frozen repository source with no gold-derived pool, the lead is 5.0 points. On AMA-Bench it answers from a 76.2% smaller answer prompt, with accuracy 2.08 points above that benchmark's own memory agent. Removing one required group from an otherwise complete set costs 12.3 and 11.1 points of repair-localization precision under two executors. Retrieval for agents is better posed as recovering what a decision lacks than re-ranking what an issue resembles.