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https://github.com/agentscope-ai/ReMe.git
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60564d5d54
Move session-transcript compression from LME hard-coded injection to a runtime context flag set by evaluation.compress_session in each benchmark config. Compression is off by default for both BEAM and LME, and BaseAgenticAnswerStep now conditionally injects the _search compress payload only when the flag is truthy.
33 lines
1.6 KiB
YAML
33 lines
1.6 KiB
YAML
# LongMemEval evaluation configuration
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# This file controls what/how to evaluate.
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dataset:
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path: "benchmark/longmemeval/dataset/longmemeval_s_reme_cleaned.json"
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start_index: 0 # first item index
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num_items: 500 # how many items to evaluate (starting from start_index)
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max_sessions: 0 # 0 = all sessions; >0 = limit sessions per item for testing
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question_types: [] # filter by question_type; empty list = no filtering (all types)
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workspace_root: "benchmark/longmemeval/workspaces/longmemeval-s" # workspace root for item workspaces
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evaluation:
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# LLM-as-judge uses the 'judge' as_llm component defined in lme.yaml
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# Model and credentials are configured there (reading from .env)
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# Judgment is always binary (yes/no) — defined in lme/llm_judge.yaml
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num_workers: 32 # 0 = auto (cpu_count - 2, min 1); 1 = sequential; >1 = parallel
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filter_future_sessions: true # true = only ingest sessions with timestamp <= question_date
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compress_session: false # true = compress session chunks in search_v2 (query-aware); false = no compression
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reme:
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config: "lme.yaml" # reme config to use (in reme/config/)
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# Dream trigger: when gap between consecutive sessions crosses this hour (23:00)
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dream_trigger_hour: 23
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# Dream scan_days for each trigger
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dream_scan_days: 2
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dream_max_units: 5
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output:
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dir: "benchmark/longmemeval/results"
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log_dir: "logs" # log directory (relative to project root)
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log_prefix: "longmemeval" # benchmark name used in log filenames
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log_to_console: true
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log_to_file: true
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