@inproceedings{hooda-etal-2026-multi,
title = "Multi-View Structural Interpretability of Agentic Reasoning Traces",
author = "Hooda, Ritika and
Machcha, Sravanthi and
MacDonald, Jackson and
Srinivasaraghavan, Lavanika and
Nijasure, Atharva and
Wang, Rui and
Shi, Lei and
Wu, Junda and
Yu, Tong",
editor = "Atanasova, Pepa and
Haffari, Gholamreza and
Wang, Minghan",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-industry.222/",
pages = "3712--3740",
ISBN = "979-8-89176-460-6",
abstract = "Agentic language models solve tasks through reasoning, tool calls, observations, and terminal answers, exposing rich process information beyond the final answer. For practitioners, knowing which process patterns lead to success or failure is essential to diagnose agent errors and build better process reward models without relying on final-answer supervision. However, existing analyses often evaluate only final outcomes or, when examining traces, flatten them into homogeneous step sequences, fragmenting each reasoning round into disconnected steps. Our key insight is that an agent{'}s reasoning, action, and observation within a round are better treated as typed segments of a single underlying process state, rather than independent steps. We propose MULTI-VIEW-RA, a reasoning abstraction that represents each round through two complementary views: accumulated state and within-round change. Using unlabeled clustering and binary outcome labels, we apply MULTI-VIEW-RA to two practical use cases: diagnosing failures by surfacing the process states that separate correct from incorrect trajectories, and providing a transition-level correctness signal that is a candidate source of process supervision. Across Math, HotpotQA, and FEVER with multiple Qwen and Llama models, MULTI-VIEW-RA outperforms step-level explainability baselines in cases with sufficient success rates, with particularly strong gains on longer, tool-rich traces."
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<abstract>Agentic language models solve tasks through reasoning, tool calls, observations, and terminal answers, exposing rich process information beyond the final answer. For practitioners, knowing which process patterns lead to success or failure is essential to diagnose agent errors and build better process reward models without relying on final-answer supervision. However, existing analyses often evaluate only final outcomes or, when examining traces, flatten them into homogeneous step sequences, fragmenting each reasoning round into disconnected steps. Our key insight is that an agent’s reasoning, action, and observation within a round are better treated as typed segments of a single underlying process state, rather than independent steps. We propose MULTI-VIEW-RA, a reasoning abstraction that represents each round through two complementary views: accumulated state and within-round change. Using unlabeled clustering and binary outcome labels, we apply MULTI-VIEW-RA to two practical use cases: diagnosing failures by surfacing the process states that separate correct from incorrect trajectories, and providing a transition-level correctness signal that is a candidate source of process supervision. Across Math, HotpotQA, and FEVER with multiple Qwen and Llama models, MULTI-VIEW-RA outperforms step-level explainability baselines in cases with sufficient success rates, with particularly strong gains on longer, tool-rich traces.</abstract>
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%0 Conference Proceedings
%T Multi-View Structural Interpretability of Agentic Reasoning Traces
%A Hooda, Ritika
%A Machcha, Sravanthi
%A MacDonald, Jackson
%A Srinivasaraghavan, Lavanika
%A Nijasure, Atharva
%A Wang, Rui
%A Shi, Lei
%A Wu, Junda
%A Yu, Tong
%Y Atanasova, Pepa
%Y Haffari, Gholamreza
%Y Wang, Minghan
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-460-6
%F hooda-etal-2026-multi
%X Agentic language models solve tasks through reasoning, tool calls, observations, and terminal answers, exposing rich process information beyond the final answer. For practitioners, knowing which process patterns lead to success or failure is essential to diagnose agent errors and build better process reward models without relying on final-answer supervision. However, existing analyses often evaluate only final outcomes or, when examining traces, flatten them into homogeneous step sequences, fragmenting each reasoning round into disconnected steps. Our key insight is that an agent’s reasoning, action, and observation within a round are better treated as typed segments of a single underlying process state, rather than independent steps. We propose MULTI-VIEW-RA, a reasoning abstraction that represents each round through two complementary views: accumulated state and within-round change. Using unlabeled clustering and binary outcome labels, we apply MULTI-VIEW-RA to two practical use cases: diagnosing failures by surfacing the process states that separate correct from incorrect trajectories, and providing a transition-level correctness signal that is a candidate source of process supervision. Across Math, HotpotQA, and FEVER with multiple Qwen and Llama models, MULTI-VIEW-RA outperforms step-level explainability baselines in cases with sufficient success rates, with particularly strong gains on longer, tool-rich traces.
%U https://aclanthology.org/2026.emnlp-industry.222/
%P 3712-3740
Markdown (Informal)
[Multi-View Structural Interpretability of Agentic Reasoning Traces](https://aclanthology.org/2026.emnlp-industry.222/) (Hooda et al., EMNLP 2026)
ACL
- Ritika Hooda, Sravanthi Machcha, Jackson MacDonald, Lavanika Srinivasaraghavan, Atharva Nijasure, Rui Wang, Lei Shi, Junda Wu, and Tong Yu. 2026. Multi-View Structural Interpretability of Agentic Reasoning Traces. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 3712–3740, Budapest, Hungary. Association for Computational Linguistics.