What is recorded here?
An agent may repeatedly make the same mistake even when a previous attempt exposed it. ReasoningBank turns self-judged successful and failed trajectories into short strategy records with a title, description and content. Later tasks retrieve relevant records, and new experiences add further lessons. Memory-aware test-time scaling, or MaTTS, compares parallel attempts or uses sequential refinement to obtain richer experience. Ouyang and colleagues test the combined approach on web navigation and software issues. The contribution is the particular strategy-memory and scaling method, not the general idea that failures can teach useful lessons.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Success and failure labels come from a model and can be noisy. Reduced interaction steps do not include all memory preparation or token costs. MaTTS spends extra computation, and best-of-several results differ from a single attempt. Some web task success rates remain low despite better step-level metrics. Failure on one attempt does not show that a route is impossible. This is not verified autonomous lifelong learning, an adversarially safe memory service or a separate discovery for each tested model.
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People and systems
Siru Ouyang; Jun Yan; I-Hung Hsu; Yanfei Chen; Ke Jiang; Zifeng Wang; Rujun Han; Long T. Le; Samira Daruki; Xiangru Tang; Vishy Tirumalashetty; George Lee; Mahsan Rofouei; Hangfei Lin; Jiawei Han; Chen-Yu Lee; Tomas Pfister
Source-listed human researchers and engineers for the exact method or resource. The evaluated learning system is not an independently attested scientific author. Earlier methods, funding and data-collection roles remain separate. Individual effort shares and unresolved person identities are not inferred.
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English catalogue narrative
ReasoningBank: learning reusable strategies from successes and failures
An agent may repeatedly make the same mistake even when a previous attempt exposed it. ReasoningBank turns self-judged successful and failed trajectories into short strategy records with a title, description and content. Later tasks retrieve relevant records, and new experiences add further lessons. Memory-aware test-time scaling, or MaTTS, compares parallel attempts or uses sequential refinement to obtain richer experience. Ouyang and colleagues test the combined approach on web navigation and software issues. The contribution is the particular strategy-memory and scaling method, not the general idea that failures can teach useful lessons.
Scientific scope
Success and failure labels come from a model and can be noisy. Reduced interaction steps do not include all memory preparation or token costs. MaTTS spends extra computation, and best-of-several results differ from a single attempt. Some web task success rates remain low despite better step-level metrics. Failure on one attempt does not show that a route is impossible. This is not verified autonomous lifelong learning, an adversarially safe memory service or a separate discovery for each tested model.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Siru Ouyang; Jun Yan; I-Hung Hsu; Yanfei Chen; Ke Jiang; Zifeng Wang; Rujun Han; Long T. Le; Samira Daruki; Xiangru Tang; Vishy Tirumalashetty; George Lee; Mahsan Rofouei; Hangfei Lin; Jiawei Han; Chen-Yu Lee; Tomas Pfister
Source-listed human researchers and engineers for the exact method or resource. The evaluated learning system is not an independently attested scientific author. Earlier methods, funding and data-collection roles remain separate. Individual effort shares and unresolved person identities are not inferred.