mirror of
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23d4c96c15
* chore(benchmark): isolate dataset/workspaces/results per benchmark
- Move shared benchmark/{datasets,memory_workspaces,results} into per-benchmark subdirs benchmark/<name>/{dataset,workspaces,results}
- Update beam/longmemeval config.yaml and run.py path defaults
- Relocate longmemeval download.py to benchmark/longmemeval/ (downloads into dataset/ subdir); inline dataset download docs into README
- Update .gitignore: benchmark/*/{dataset,workspaces,results}/
- Move result-{beam,longmemeval}.md to benchmark/results_md/ and drop result- prefix; update README links
- Fix stale path refs in llm_judge.py and logs/demo_search_format.py
* feat(benchmark): add read tool to agentic answer and update BEAM results
- Add 'read' to job_tools in BaseAgenticAnswerStep for file reading capability
- Document read tool usage in lme/agentic_answer.yaml system prompt
- Update result-beam.md with latest evaluation scores (OVERALL: 0.623/0.580)
* feat(auto_memory): add source line-number markers for note traceability
- Add _format_history hook in AutoMemoryStep with line-number annotation
- Override in BeamAutoMemoryStep to prefix each turn with [Ln] for citation
- Add session_file variable to prompt templates for source marker paths
- Simplify repeated extraction rules by referencing system prompt
- Enhance agentic_answer search strategy (multi-search, read tool hint)
- Add warning log on ReadStep failure
* feat(beam): enhance auto_memory with source markers and pilot ingest tooling
* refactor(beam): rename max_chunk_words to max_segment_words, drop one-off pilot scripts
* feat: add CompressorStep and search_v2 dual-mode session compression
- Add CompressorStep (reme/steps/evolve/compressor.py) for direct LLM
text compression with optional query-guided relevance filtering
- Extend search_v2_step to support query-aware and query-independent
session transcript compression via _compress injected kwargs
- Refactor _source_format.py: split into render_chunk_entries +
join_chunk_entries; session chunks now render line-aligned with
L<n>: prefixes for verbatim/compressed parity
- Add JOB_TOOLS and INJECTED_JOB_KWARGS to BaseAgenticAnswerStep for
per-subclass tool and parameter injection
- LmeAgenticAnswerStep injects _search._compress payload to enable
query-aware compression during benchmark evaluation
- Record compression ablation results in result-longmemeval.md
- Add unit tests for CompressorStep and search compression paths
* refactor(compress): relax session compression to lenient format-preserving strategy and update LME results
* refactor(benchmark): make session compression config-driven via compress_session flag
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.
* feat(lme/auto_memory): add source attribution markers with line numbers
Add _format_history to annotate each turn with [Ln] line numbers and
expose {session_file} in prompts so the agent can emit bare wikilink-style
source markers like [[session/dialog/s1.jsonl#L1-L2,L5-L6]] at the end
of factual entries. Consolidate the per-prompt body/format rules into
references to the system prompt to avoid drift, and add frontmatter-
protection guidance for the edit tool.
* feat: improve agentic answer prompt and update beam 100K results
- Strengthen abstention rule: prohibit extrapolation from related but
non-direct evidence
- Add multi-angle search after preliminary answer to check for
conflicting/supplementary/updated information
- Add max-iteration fallback to 'Information not found'
- Update beam.md with 100K results (agentscope 2.0.4.post1, from scratch)
including per-type token consumption and memory construction stats
- config.yaml: 100K dataset, 20 workers for BEAM evaluation
- run.py: add memory construction token usage tracking (default agent)
- Overall: 0.635 → 0.654 (+0.019), contradiction_resolution: 0.338 → 0.478
(+0.140), abstention: 0.500 → 0.525 (+0.025)
* feat(read): add session-aware formatting for read tool and update BEAM eval
- Add truncate_session_output in _file_io.py to render jsonl session
lines as [speaker @ time] content before byte-budget truncation
- Add read_step_format_session flag to ReadStep, honoring injected
job kwargs (precedence) and YAML fallback
- Inject read_step_format_session=True into BaseAgenticAnswerStep
so agentic answer reads render session transcripts human-readably
- Refine BEAM agentic_answer prompt: continue multi-angle search
after preliminary answer, forbid fabrication/extrapolation
- Update BEAM config to 1M variant and add sequential 100K-eval /
1M-build shell script
- Refresh benchmark/results_md/beam.md with latest results
* chore(config): disable expand_links in beam and lme search_v2 configs
* refactor(beam): drop one-off sequential 100K-eval-then-1M-build script
* fix(benchmark): add compressor job to beam config and fix BEAM clone instructions
- Add compressor job and compressor as_llm component to reme/config/beam.yaml
(aligned with lme.yaml) so that compress_session: true works for BEAM
- Add graceful degradation guard in search_v2._compress_session_entries:
when the compressor job is missing from the active config, log a warning
and skip compression instead of raising 'Job compressor not found'.
Skipped when there is no app_context so unit tests mocking run_job still
drive compression behavior.
- Fix BEAM download instructions in README.md/README_ZH.md: add mkdir -p
before cd benchmark/beam/dataset (the directory is gitignored and absent
in a fresh clone)
* fix(steps): guard compressor exceptions and fix ReadStep boolean override
1. search_v2: catch per-entry exceptions from run_job('compressor') inside
compress() so asyncio.gather never propagates a compressor failure (e.g.
temporary LLM outage). The failing entry keeps its original body while
remaining entries are still compressed, preserving already-retrieved
search results.
2. read: replace 'context_value or yaml_value' with an existence check so
that a runtime-injected False can explicitly disable a YAML-true
read_step_format_session flag.
Add focused unit tests for both paths.
* fix(search_v2): use existence check for strict_date_filter boolean override
Replace 'context_value or yaml_value' with an existence-based check so
that a runtime-injected False can explicitly disable a YAML-true
strict_date_filter flag, consistent with the read_step_format_session fix.
* refactor(search): simplify strict_date_filter fallback to truthiness-or
* style(test): rename unused param to satisfy pylint W0613
* refactor(benchmark): isolate per-benchmark assets and simplify LME agentic prompt
- Move shared benchmark/README, README_ZH, kill.sh, and results_md/*.md into
per-benchmark subdirs (benchmark/beam/, benchmark/longmemeval/) so each
benchmark owns its own docs, scripts, and result snapshots.
- Simplify lme/agentic_answer.yaml system prompt: drop verbose memory-system
description, keep search strategy, draft tool, and answer rules concise.
* docs(benchmark): update LME README_ZH results to latest eval run
---------
Co-authored-by: sa-buc <jiangniurou.xyf@dail-algo011164204033.ET135>
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|---|---|---|
| .. | ||
| config.yaml | ||
| kill.sh | ||
| README.md | ||
| README_ZH.md | ||
| run.py | ||
BEAM Benchmark
BEAM is a benchmark for memory capability over long-context chat cases. Each
case contains a very long chat history split into batches; ReMe converts each
batch into a session, ingests them in chronological order, then answers probing
questions via an agentic (ReAct) mode. Answers are scored with BEAM's
rubric-based answer_judge job, which produces both a graded score and a binary
verdict, and per-type averages are reported.
BEAM ships dataset variants by chat size — 100K / 500K / 1M / 10M — so
memory systems can be stressed at different context lengths. Question types
include abstention, contradiction resolution, event ordering, information
extraction, instruction following, knowledge update, multi-session reasoning,
preference following, summarization, and temporal reasoning.
For the shared setup (dependencies, credentials, log conventions) see the top-level benchmark README.
1. Get the Dataset
BEAM is a public repository, cloned into benchmark/beam/dataset/:
mkdir -p benchmark/beam/dataset
cd benchmark/beam/dataset
git clone https://github.com/mohammadtavakoli78/BEAM.git
After cloning, benchmark/beam/dataset/BEAM/ should contain chats/, src/,
topics/ and other subdirectories.
2. Run
From the repository root:
python benchmark/beam/run.py
python benchmark/beam/run.py --config benchmark/beam/config.yaml
python benchmark/beam/run.py -q # quiet
python benchmark/beam/run.py --eval_only # reuse existing workspaces, query + judge only
3. Pipeline
- For each case, load
chat.jsonand convert each batch into a ReMe session. - Ingest sessions in chronological order into an isolated workspace, then
digest_update. - Answer each probing question via agentic (ReAct) mode.
- Score answers with BEAM's rubric-based
answer_judgejob and print per-type averages.
4. Key config — benchmark/beam/config.yaml
| Key | Meaning |
|---|---|
dataset.beam_root |
BEAM dataset root (benchmark/beam/dataset/BEAM). |
dataset.chat_size |
Variant to run: 100K / 500K / 1M / 10M. |
dataset.case_ids |
Specific cases (e.g. ["1","2"]); empty = all cases. |
dataset.start_index / num_items |
Case pagination (num_items 0 = all). |
dataset.workspace_root |
Per-case workspace root (benchmark/beam/workspaces/beam). |
evaluation.num_workers |
0 = auto, 1 = sequential, >1 = parallel. |
reme.config |
ReMe config used (beam.yaml). |
output.dir |
Results directory (benchmark/beam/results). |
5. Outputs
Results are JSON files written to output.dir as
results_<chat_size>_<timestamp>.json, with a per-type score summary also
printed to the console. Logging conventions are shared across benchmarks — see
the top-level README.
6. Reference Results
The results below use the longmemeval-version prompt.
100K
agentscope==2.0.4.post1, conda reme env, 20 workers, eval-only (reusing prebuilt memory) (2026-08-05, 20 cases / 400 Qs, total 46.0 min)
| Type | Agentic | Binary | input tok/q | output tok/q | total tok/q | tool calls/q |
|---|---|---|---|---|---|---|
| abstention | 0.550 | 0.550 | 96,031 | 1,070 | 97,101 | 4.58 |
| contradiction_resolution | 0.438 | 0.412 | 32,263 | 872 | 33,135 | 2.48 |
| event_ordering | 0.501 | 0.423 | 140,195 | 5,163 | 145,358 | 4.70 |
| information_extraction | 0.873 | 0.832 | 50,245 | 883 | 51,128 | 3.15 |
| instruction_following | 0.750 | 0.725 | 37,986 | 848 | 38,834 | 2.67 |
| knowledge_update | 0.688 | 0.675 | 31,198 | 651 | 31,849 | 2.27 |
| multi_session_reasoning | 0.626 | 0.584 | 85,038 | 4,563 | 89,601 | 4.28 |
| preference_following | 0.925 | 0.912 | 34,281 | 989 | 35,270 | 2.50 |
| summarization | 0.623 | 0.461 | 89,657 | 2,056 | 91,713 | 4.12 |
| temporal_reasoning | 0.637 | 0.625 | 34,563 | 1,049 | 35,612 | 2.52 |
| OVERALL | 0.661 | 0.620 | 63,146 | 1,814 | 64,960 | 3.33 |
Memory Construction average token consumption (default agent, full build over 20 cases):
| Agent | input tok/case | output tok/case | total tok/case |
|---|---|---|---|
| default | 2,172,316 | 136,697 | 2,309,013 |
1M
agentscope==2.0.4.post1, conda reme env, 20 workers, full memory build (2026-08-05, 35 cases / 700 Qs, total 459.2 min)
| Type | Agentic | Binary | input tok/q | output tok/q | total tok/q | tool calls/q |
|---|---|---|---|---|---|---|
| abstention | 0.429 | 0.429 | 118,707 | 1,178 | 119,886 | 4.20 |
| contradiction_resolution | 0.391 | 0.364 | 49,787 | 810 | 50,597 | 2.50 |
| event_ordering | 0.558 | 0.456 | 201,514 | 3,889 | 205,403 | 4.79 |
| information_extraction | 0.809 | 0.772 | 78,950 | 894 | 79,844 | 3.00 |
| instruction_following | 0.852 | 0.832 | 55,757 | 924 | 56,681 | 2.81 |
| knowledge_update | 0.779 | 0.771 | 45,981 | 665 | 46,646 | 2.37 |
| multi_session_reasoning | 0.658 | 0.612 | 138,133 | 2,873 | 141,006 | 4.40 |
| preference_following | 0.798 | 0.777 | 51,796 | 920 | 52,716 | 2.53 |
| summarization | 0.693 | 0.537 | 158,794 | 2,905 | 161,700 | 4.44 |
| temporal_reasoning | 0.536 | 0.536 | 100,176 | 3,148 | 103,324 | 3.90 |
| OVERALL | 0.650 | 0.609 | 99,959 | 1,821 | 101,780 | 3.49 |
Memory Construction average token consumption (default agent, full build over 35 cases):
| Agent | input tok/case | output tok/case | total tok/case |
|---|---|---|---|
| default | 31,943,817 | 1,417,061 | 33,360,878 |