Unassigned · 2026-10-06
Agentic Cognitive Depth: Operational Criteria for Evaluating LLM Agents
Nijesh Upreti, Chris Sypherd, Vaishak Belle
AI-assisted summary based on the paper. Consult the linked source for methods and limitations. Benchmark numbers appear after editor verification.
TL;DR
The authors define “agentic cognitive depth” as a trajectory-level profile across five operational criteria: context sensitivity (C), temporal continuity (T), multimodal coordination (M), adaptive interaction (A), and metacognitive monitoring (Mc). They provide operational proxies and a perturbation procedure for each criterion, and connect the profile to an agent’s world-model substrate to support per-criterion diagnostics beyond end-to-end task success.
Why it matters
End-to-end agent benchmarks can hide where an agentic loop fails across Planning, Memory, Tools, and Control Flow. The paper proposes trajectory-level criteria with measurable proxies and perturbations, plus a mapping to a maintained “world model.” This enables extending existing benchmarks (e.g., GAIA, SWE-bench, WebArena, TRIP-Bench) with diagnostics that localize failure modes and inform evaluation and design changes (e.g., symbolic verification, structured memory, planner coupling, constrained tool routing).
Method
- Define a trajectory-level profile D(s,u)=(C,T,M,A,Mc) with each component normalized to [0,1], measured via observable input/output behavior using operational proxies and perturbations.
- For each criterion, specify operational proxies and a perturbation procedure (Table 3) to test targeted behavioral properties across the run.
- Link diagnostic criteria to the agent’s maintained “world model” and its update loop (non-monotonic belief revision + write-back) so low scores can be inspected mechanistically.
Limitation
The authors state: “Our work is limited to measurement. We treat the five criteria as operational properties of agentic trajectories. A full account of cognition would require a broader theory and different evidence. We leave the broader cognitive theory and the empirical benchmark to future work.”
Abstract (from arXiv)
Agentic large language model (LLM) systems are commonly implemented as an LLM in a loop with Planning, Memory, Tools, and Control Flow. This application-focused view connects agentic LLM research with deployable systems and leaves open how such systems should be evaluated beyond end-to-end task success. Building on this view, we define agentic cognitive depth as a trajectory-level profile across five operational criteria. The profile contains context sensitivity ($C$), temporal continuity ($T$), multimodal coordination ($M$), adaptive interaction ($A$), and metacognitive monitoring ($Mc$). The first four criteria measure how well Control Flow, Memory, Tools, and Planning are used across a trajectory. The fifth measures whether the system monitors and regulates the full run. For each criterion, we give operational proxies and a perturbation procedure, then connect the profile to the agent's world model. We provide the structure needed to extend benchmarks such as GAIA, SWE-bench, WebArena, and TRIP-Bench with per-criterion diagnostics. Symbolic verifiers, structured memory, planner coupling, and tool constraints provide practical ways to build and test these capacities.
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Publication record
Bibliographic evidence describes where and when a record appeared. It does not validate scientific claims.
First public release: 2026-10-03 · Indexed by Pub-AI: 2026-10-06 UTC
Agentic Cognitive Depth: Operational Criteria for Evaluating LLM Agents
Preprint posted: 2026-10-03 · Original record ↗
- arxiv
- 2610.04168
Explicit source versions (1)
- v1 · 2026-10-03
- Preprint available
Source date: 2026-10-03 · First detected: 2026-10-25 UTC · Historical baseline
Source evidence (1)
- Official evidence ↗ · atom:published (official metadata)
Abstract · arxiv
Agentic large language model (LLM) systems are commonly implemented as an LLM in a loop with Planning, Memory, Tools, and Control Flow. This application-focused view connects agentic LLM research with deployable systems and leaves open how such systems should be evaluated beyond end-to-end task success. Building on this view, we define agentic cognitive depth as a trajectory-level profile across five operational criteria. The profile contains context sensitivity ($C$), temporal continuity ($T$), multimodal coordination ($M$), adaptive interaction ($A$), and metacognitive monitoring ($Mc$). The first four criteria measure how well Control Flow, Memory, Tools, and Planning are used across a trajectory. The fifth measures whether the system monitors and regulates the full run. For each criterion, we give operational proxies and a perturbation procedure, then connect the profile to the agent's world model. We provide the structure needed to extend benchmarks such as GAIA, SWE-bench, WebArena, and TRIP-Bench with per-criterion diagnostics. Symbolic verifiers, structured memory, planner coupling, and tool constraints provide practical ways to build and test these capacities.