Metraipolis › archive › dc_272ebd88cd07

CREDO: Evidence-Conditioned Credit Assignment for Temporal Reasoning Agents — arXiv:2611.01643

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.01643
License (as archived): CC0-1.0
Archived: 2026-11-03T10:36:04.645Z
SHA-256 of the archived text: 2d2eb85100e42086a616b094b3b89fd6593bd5ae648a3c564df1a3314eb6ac19
Stable URL of this page: https://metraipolis.com/documento/dc_272ebd88cd07

How to cite

CREDO: Evidence-Conditioned Credit Assignment for Temporal Reasoning Agents — arXiv:2611.01643 — arXiv (en), https://arxiv.org/abs/2611.01643 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_272ebd88cd07 (Metraipolis archive dc_272ebd88cd07, sha256 2d2eb85100e42086a616b094b3b89fd6593bd5ae648a3c564df1a3314eb6ac19)

BibTeX

@misc{dc272ebd88cd07,
  title = {CREDO: Evidence-Conditioned Credit Assignment for Temporal Reasoning Agents — arXiv:2611.01643},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.01643},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_272ebd88cd07; sha256 2d2eb85100e42086a616b094b3b89fd6593bd5ae648a3c564df1a3314eb6ac19},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.01643. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Temporal Knowledge Graph Question Answering (TKGQA) requires language model agents to retrieve evidence, select relevant facts, and perform multi-step temporal reasoning. However, the trajectory-level outcome signal in GRPO does not explicitly distinguish the quality of intermediate decisions: downstream failures can penalize useful evidence acquisition, while identical rewards in all-failed groups eliminate the relative outcome signal despite differences in intermediate progress. We propose Credit Redistribution with Evidence-Driven Opportunities (CREDO), which uses intermediate evidence to recover local supervision, including in all-failed groups, and determine where outcome supervision acts. CREDO assigns positive stage-level credit through comparisons among rollouts of the same question and uses retained answer overlap as an opportunity proxy to shift part of the outcome-policy weight toward reasoning and answering. The framework preserves terminal rewards and trajectory-level advantages, while redistribution conserves total outcome-policy weight per trajectory. Following the public evaluation protocol of prior work, CREDO raises Overall accuracy from 78.0% to 82.4% and Multiple accuracy from 55.0% to 65.6% on MultiTQ relative to the strongest published baseline in our comparison. On TimelineKGQA, it raises cross-KG accuracy from 30.9% to 36.4% relative to our reproduced baseline via direct transfer to ICEWS Actor under matched training and evaluation settings.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR).
Pagina del lavoro: https://arxiv.org/abs/2611.01643 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T10:29:48.795Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).