datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
funes-handoff-recall-benchmark
handover-vs-recall
A long investigation bloats an agent session until each new turn costs more to carry the context than to
do the work. Switching to a fresh session avoids that — but the findings have to travel somehow, and the
ways of moving them differ in cost. This benchmark measures those ways, as cost per successful task,
on tasks that genuinely require the prior investigation:
arm
channel
A branch-only
switch, carry nothing — the fresh session re-derives the… See the full description on the dataset page: https://huggingface.co/datasets/dacorvo/funes-handoff-recall-benchmark.funes-recall-session-pi-traces
dacorvo/funes-recall-session-pi-traces
pi coding-agent session traces produced by
agentcap runs. Each run
contributes one folder under data/<run_id>/; inside, one file per
session in pi's native export format.
The on-the-wire HTTP captures for these same runs live in
dacorvo/funes-recall-session-captures.
Both belong to the
funes-recall-session Collection
— join on run_id to align captures with traces.
recall-rewrite-oasst1
Recall Rewrite OASST1: knowledge-aligned SFT data
Data release for the paper "Stick to What You Know: A Study of Knowledge-Aligned Supervised Fine-Tuning"
(Becker, Kemmler, Thulke, Schäfer, Dugast, Ney; accepted at EMNLP 2026, Main Conference).
Knowledge-aligned SFT constrains supervised fine-tuning targets to what the base model already knows.
Recall Rewrite implements this without external evidence: every gold response of the SFT set is
decomposed into atomic claims, each… See the full description on the dataset page: https://huggingface.co/datasets/apptek-com/recall-rewrite-oasst1.funes-recall-session-hermes-traces
dacorvo/funes-recall-session-hermes-traces
hermes coding-agent session traces produced by
agentcap runs. Each run
contributes one folder under data/<run_id>/; inside, one file per
session in hermes's native export format.
The on-the-wire HTTP captures for these same runs live in
dacorvo/funes-recall-session-captures.
Both belong to the
funes-recall-session Collection
— join on run_id to align captures with traces.
han-long-term-recall-benchmarks-v1
Humanoid Long-Term Recall Benchmarks
This dataset evaluates how effectively
humanoid agents retrieve stored knowledge
after extended operation.
Contents
Memory age
Recall accuracy
Retrieval latency
Use Cases
Memory durability testing
Cognitive performance evaluation
System optimization
Part of
Humanoid Network (HAN)
License
MIT
agentic-state-recall-v1
Agentic State Recall v1 (ENERZAi 내부, 2026-09-22)
실제 에이전트 궤적(AgentTuning alfworld·webshop, nebius SWE-agent; neulab/agent-data-collection 표준화본)에서 개체별 상태 변화를 프로그램으로 복원해 구조화 정답을 만들고,
Qwen3.8-27B 가 자연어로 문장화한 뒤 역파싱 검증·근거 게이트·번호 정합 게이트를 통과한 문항만 남긴 학습 데이터. AMA-Bench 의 네 유형(A 회상 · B 인과 · C 상태 갱신 · D 상태 추상화)을 겨냥한다.
B 유형의 "왜"·"실패 뒤 다음 행동" 문항은 궤적에 기록된 에이전트의 이유(Thought) 를 근거로 한다.
프롬프트는 AMA compaction_v3_nostate 하네스 형식(Task / Step Index / Most Recent(Thought: 줄 포함) / Recalled / Questions /… See the full description on the dataset page: https://huggingface.co/datasets/HBKenerzai/agentic-state-recall-v1.recall_intelligence_signals
Recall Intelligence Signals v1
Public recall and enforcement records prepared by SingleFoundry. Built for institutional_research buyers, this SingleFoundry data product packages 1 validated records with governed source evidence, quality checks, audit traceability, and ready-to-use delivery metadata.
Dataset Files
data/recall-intelligence-signals-extracted-records-csv.csv: latest validated SingleFoundry CSV package.
singlefoundry-metadata.json: release metadata… See the full description on the dataset page: https://huggingface.co/datasets/singlefoundry/recall_intelligence_signals.