$A^2E$ : An End-to-End Agent Auditing Engine

Chenyue Yu; Guanchu Wang; Haoning Wang; Mingxun Zhang; Na Zou; Xia Hu; Yingjun Shang

REVIEW 5 major objections 6 minor 25 references

$A^2E$ : An End-to-End Agent Auditing Engine

T0 review · 5 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read A2E shows that agent harnesses differ sharply in process even when they share the same model and score alike on correctness, and that no single harness wins across every task.

desk verdict A genuinely useful evaluation engine with a clean protocol abstraction, but the headline empirical findings rest on 5-task cells and unverified trace comparability. read the letter →

arxiv 2608.07346 v2 pith:FU6KBTTD submitted 2026-08-07 cs.AI

Haoning Wang , Mingxun Zhang , Chenyue Yu , Yingjun Shang , Xia Hu , Guanchu Wang , Na Zou This is my paper · ORCID

classification cs.AI

keywords agentevaluationharnessTaskProtocollifecycle-alignedtrajectorymonitoringLLMagentsOpenTelemetryspans

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that agent harnesses — the software that turns an LLM into a working agent — can be evaluated end-to-end in a way that is systematic, extensible, and sensitive to process, not just final answers. Its engine, A2E, uses a task protocol so that any of nine harnesses can be paired with any of 23 benchmarks without custom integration code. Running that 9x23 grid shows that no harness–model combination wins across all tasks, and that correctness alone (which spans only 0.57–0.68 across harnesses) hides large differences in planning, tool use, and token cost. This matters because harness design choices are as influential as model choice, and a reproducible trace-based audit tells developers where those differences come from.

What carries the argument

The Agent Task Protocol (ATP) is the internal software interface that separates benchmark adaptation from harness execution, exposing four objects — TaskInput, AgentBinding, AgentRunner, and TaskTrace — so one benchmark adapter can pair with any registered harness runner with no per-combination code. On top of that, the Monitor records every run as an OpenTelemetry-compatible span tree, and the Evaluation Layer registers each metric under a lifecycle stage (Reasoning, Action, Final Answer, Runtime Quality) and a fine-grained dimension. Together these carry the argument: a uniform task interface, faithful traces, and stage-aligned metrics turn a 9-by-23 grid into comparable, re-scoreable evidence about where harnesses actually differ.

What would settle it

Re-run one harness's tasks while logging token usage independently of the span data (for example, from the raw model API response), and compare it to A2E's recorded total_token_usage; if the two disagree, or if two different SDK adapters for the same harness produce different process-metric values on identical runs, the cross-harness spreads are instrumentation artifacts.

Watch

Extended reading notes

Core claim

The central claim is that a harness is not a lightweight wrapper around an API model: with the same backbone model and identical inference settings, nine agent frameworks earn average correctness scores between 0.57 and 0.68 yet differ by a factor of 3.5 in mean token usage, from about 2,063 to 7,319 tokens per run, and by wide margins on planning, tool invocation, and other process dimensions. A2E makes these differences visible by recording each run as a standardized span tree and scoring it with lifecycle-aligned metrics, so a harness is described by a profile across reasoning, action, answer, and runtime quality rather than by a single outcome number. The paper further claims that no single combination consistently outperforms all others across every task; the best harness changes across benchmarks and models, so harness effectiveness is model- and task-dependent rather than globally rankable.

Load-bearing premise

The comparisons are only as good as the traces: every harness must be instrumented faithfully and uniformly, so a difference in a process metric reflects harness behavior rather than a difference in what the monitor happened to record.

Editorial extensions

If this is right

Adding a new benchmark or harness requires only the ATP adapter for that side; monitoring, evaluation, and storage are reused unchanged.
Harness rankings found on one benchmark or model will not transfer to another, so aggregate leaderboards that average across tasks will mislead.
Trajectory-level metrics such as planning, tool use, and token cost separate harnesses more sharply than correctness, so harness evaluation should report a profile rather than a single score.
Because trajectories live in a database instead of log files, a newly defined metric can be computed over old runs without re-executing the agent or paying for new API calls.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If trace fidelity differs across harnesses, the reported token spread and several process-metric comparisons should be read as lower bounds on harness variation; a calibration study against an independent log would tighten the claim.
The no-universal-winner conclusion uses only five tasks per benchmark cell, so per-cell variance is high; a powered follow-up with more tasks or bootstrapped confidence intervals would test whether the conclusion survives.
The lifecycle taxonomy could support automated failure attribution — locating the stage where a run breaks and suggesting where to fix the harness — which the paper demonstrates but leaves implicit.
The Qh sweet-spot score could be trivially extended with user-supplied weights on cost versus success, turning it into a deployer-specific ranking rather than a fixed trade-off.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The punchline: A2E is a real, runnable engineering artifact, and the ATP protocol is a genuinely useful abstraction — but the empirical claims about harness differences rest on 5-task cells and unverified trace comparability, so treat the numbers as illustrative rather than established.

What's actually new: the system combines OpenTelemetry/OpenInference tracing, a benchmark-harness separation layer (ATP), and a lifecycle-aligned metric taxonomy in one stack. That combination, plus the demonstrated 9×23 grid with 1035 scored runs, is a concrete contribution to agent-evaluation infrastructure. The paper is admirably transparent about its own limits: it says the table is not meant to rank frameworks, admits the 0.20 resolution, flags the two instrumentation-determined rows in Figure 6, re-scored the 44 empty-extractor runs, and excludes crewai from the token analysis. That honesty is rare and useful.

The soft spots: the headline findings rest on very small samples. Five tasks per cell gives a resolution of 0.20 with no error bars or significance tests. The LLM-as-judge metrics are unvalidated, the Q-score weighting in Eq. (1) is arbitrary, and the two main comparisons use different backbone models (DeepSeek-V4-pro in Table 1/Figure 1, GLM-5.2 in Figure 7). More fundamentally, the stress-test concern is on target: the process-metric and token findings depend on trace equivalence across harnesses, and the paper does not demonstrate that the same behavior produces comparable spans in every framework. The paper flags two known failures (crewai's missing token counts; tool_invocation and hallucination being instrumentation-set), but those are only the visible tip; the remaining process metrics are also judged from span data whose cross-harness equivalence is unverified. The no-dominance claim is less vulnerable because correctness scores don't depend on span completeness, but the efficiency and process-diagnosis conclusions are not yet supported. Section 6.2's "strong discriminative capability of our agent evaluation engine" is also a bit circular, since the engine is showing itself capable of finding differences.

Who should read it: anyone building agent evaluation infrastructure will find the ATP protocol and the metric taxonomy worth studying. The empirical results are a useful sanity check for the field, but they don't yet support the stronger claim that process metrics reliably distinguish harnesses.

Recommendation: send it to peer review. A serious referee will ask for significance testing, judge validation, and a span-equivalence study, and the paper will be stronger for it. The engineering contribution is real and deserves to be in the literature with the empirical claims properly hedged.

Referee Report

5 major / 6 minor

Summary. This paper presents A2E, an end-to-end agent auditing engine composed of three layers: a Task Layer built on a newly proposed Agent Task Protocol (ATP) that decouples benchmarks from harnesses, a Monitor Layer that captures OpenTelemetry-compatible execution traces, and an Evaluation Layer that organizes metrics into a lifecycle-aligned taxonomy. The authors execute a 9-harness by 23-benchmark grid with 5 tasks per cell (1035 scored runs) under a common DeepSeek-V4-pro backbone, and report that no harness combination dominates all benchmarks and that process metrics such as planning and tool-use quality vary more widely across harnesses than correctness alone. A second experiment with GLM-5.2 on three benchmarks and a two-trajectory case study illustrate cross-harness efficiency differences. The paper's primary contribution is the integrated, database-backed evaluation infrastructure rather than a new benchmark or a derived theoretical result.

Significance. If trace comparability and statistical grounding are established, A2E would be a useful infrastructure contribution. The ATP abstraction removes pairwise benchmark-harness integration code, the database-backed trajectory store supports incremental evaluation, and the lifecycle taxonomy provides a principled place for process metrics. The paper also ships open code, fixed seeds, and a fully executed grid of 207 cells, which are concrete reproducibility strengths. However, the empirical claims as currently stated go beyond what the evidence supports: cross-harness trace equivalence is not demonstrated, and the per-cell sample size of five tasks is very small. The paper is honest about some of these limits in the Table 1 discussion, but the abstract and later sections present stronger conclusions than that discussion permits.

major comments (5)

[§3.1, Figure 6 caption] The load-bearing assumption behind the process-metric findings is that the Monitor's SDK layer records semantically comparable spans across all nine harnesses. This is not demonstrated. Section 3.1 describes the SDK layer as per-framework adapter code, but no experiment verifies that the same model call, tool invocation, or error produces the same span content (token counts, event counts, status, boundaries) in each harness. The manuscript itself flags two breaks in uniformity: the Figure 6 caption reports that crewai's LLM spans carry no token counts and that tool_invocation and hallucination are 'set by the instrumentation rather than the agent.' Because the remaining process metrics (e.g., plan_goal_alignment, plan_completeness, conciseness) are derived from these spans, the 3.5x token spread in Figure 6b and several process-metric contrasts may be artifacts of differential instrumentation coverage. Please add a span-equivalence test (e.g., run a fixed probe task through every SDK adapter and compare token sums, span boundaries, and event counts), report per-harness instrumentation coverage, and either exclude or re-label instrumentation-confounded metrics in the headline comparisons.
[§6.1, Table 1] The headline that 'no single combination consistently outperforms all others across every task' rests on cells of five tasks with no error bars. The Table 1 caption itself states that 'the score resolution is 0.20, so per-cell variance is high,' and the text says the table is 'not meant to rank the frameworks.' Yet the abstract and Figure 7 present cross-harness rankings and token-efficiency separations without confidence intervals. With five tasks per cell, a difference of 0.20 can be a single task, and many cells are 0.00 or 1.00. The CLI already accepts seeds (§4.3), so please report at least a multi-seed or bootstrap analysis, error bars on the token and turn means in Figure 6b, and per-benchmark confidence intervals, and state the claims in proportion to this precision.
[§6.2, Eq. (1)] The Q-score used to rank harnesses in Figure 7 is a hand-selected combination of normalized token usage and success rate, with no sensitivity analysis. The text then takes the 'clear separation among harnesses' as evidence of the engine's 'discriminative capability.' This is self-referential in the sense that the engine's own metric is used to validate the engine; an independent check is needed. Please report how the Figure 7 rankings change under alternative normalizations and weights, validate the underlying LLM-judge metrics against human labels or known-effect probes, and avoid claiming discriminative capability solely from separation under a metric defined by the authors.
[§6.1 vs. §6.2] The experimental narrative uses two different backbone models: the 23-benchmark grid and Figure 6 use DeepSeek-V4-pro, while the cross-benchmark comparison (Figure 7) and case study use GLM-5.2. The abstract's claim that 'model-harness combinations exhibit substantial performance variation' suggests an interaction analysis, but the paper reports no model x harness interaction test, and the two models are evaluated on different benchmark subsets. As written, the 'no single combination consistently outperforms all others' conclusion is supported only for one model on the full grid and another model on three benchmarks. Please either restrict the generalization or add a systematic model x harness comparison on overlapping benchmarks with an appropriate interaction analysis.
[Figure 6 caption and §6.1] The re-scoring of correctness on the 44 runs where the multiple-choice Extractor returned an empty prediction is referenced in the Figure 6 caption but not described in §6.1. It is unclear whether the re-scored values replace the Table 1 correctness scores, whether re-scoring uses an LLM judge or a different extractor, and how this interacts with the 'matched' protocol. Please specify the re-scoring procedure and ensure a single, consistently applied outcome definition across Table 1, Figure 6, and Figure 7, or state explicitly when different outcome definitions are used.

minor comments (6)

[§6.1] 'One thousand thirty-five scored runs at full two hundred seven over two hundred seven coverage' is garbled; rewrite as '1035 scored runs covering all 207 harness-benchmark pairs.'
[§4.3 vs. Table 1] Section 4.3 specifies a default sample size of 40 tasks, but Table 1 uses 5 tasks per cell; explain why the experiment departs from the default and how this affects the protocol's stated reproducibility.
[Figure 6(a)] The annotation threshold of 0.10 deviation from the cross-harness mean is not justified; state the threshold in the text and either justify it or show sensitivity to the choice.
[Table 1] The abbreviations 'AutoGenA', 'Claude AS', and 'OpenAI Age' are defined only in the caption; write them in full at first use in the table itself for readability.
[§6.2, §6.3] A few mid-sentence capitalizations ('Can', 'The') appear in the narrative text; proofread these sections.
[§6.2, Eq. (1)] The normalized quantities T_h and S_h are not formally defined; specify the normalization ranges and variables used in Eq. (1).

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the central claims are empirical measurements on standard external benchmarks, and the few self-referential framings are explicitly labeled as organizational rather than derivational.

full rationale

A2E's central claims are empirical rather than derived: the 'no universally dominant harness' finding is an observation over a 9-harness by 23-benchmark grid scored on standard benchmark datasets with established scorers, and the process-metric claim is a measurement statement about trace-derived quantities. The lifecycle taxonomy is explicitly introduced as an organizational interface, not as a first-principles derivation whose output is encoded in its input: the paper says 'This catalog is not intended as a complete definition of agent quality; rather, it demonstrates how the proposed architecture organizes, executes, stores, and aggregates heterogeneous metrics across the full agent lifecycle.' Eq. (1) is a hand-chosen composite for displaying a success-efficiency trade-off; the paper does not claim it is derived from first principles, so the fact that rankings follow from the score is definitional but not circular. There are no load-bearing self-citations: references to OpenTelemetry, Inspect AI, Phoenix, AgentBoard, and AgentGym are external, and ATP is introduced in this paper rather than imported from prior author work. The strongest validity concern, trace comparability, is a measurement-fidelity issue, not circularity: the paper itself flags CrewAI's missing token counts and states that 'Rows in grey italic (tool_invocation, hallucination) are set by the instrumentation rather than the agent and should not be read as harness behaviour.' That caveat weakens some empirical comparisons, but it does not make any claimed result equivalent by construction to a fitted input or a self-citation. The only mildly self-referential framing is the statement that 'the clear separation among harnesses demonstrates the discriminative capability of our agent evaluation engine,' since the separation is measured by the engine's own metrics; however, the case study in Table 2 provides an independent, manually inspected trajectory comparison, so the claim is not forced by definition. Overall, no circular step can be exhibited with a specific equation-to-equation or fitted-parameter-to-prediction reduction, so the paper is assigned a low score reflecting only a minor self-referential validation tone rather than actual circularity.

Assumptions & free parameters 3 free parameters · 4 assumptions · 2 invented entities

The empirical conclusions rest on free choices (task sample size, seeds, Q-score weights), unfalsified assumptions about trace fidelity and LLM-judge validity, and two introduced software constructs (ATP and the lifecycle taxonomy). The paper does not ship data to test these assumptions independently.

free parameters (3)

per-cell task sample size = 5 tasks
§6.1: each benchmark contributes five tasks from a fixed seed; score resolution is 0.20 and per-cell variance is high. All reported rankings depend on this choice.
Q-score weighting = equal weights on normalized token usage and success-rate gap
§6.2 Eq. (1): the sweet-spot distance gives equal weight to normalized tokens and normalized success gap; no justification or ablation is given for this weighting, yet it determines the highlighted top-3 harnesses.
random seeds = 20260725 and 20260729
Table 1 caption: seeds for non-sandbox and sandbox benchmarks; the sampled task IDs determine every cell score.

assumptions (4)

domain assumption LLM-as-judge evaluations of plan quality, hallucination, safety, and similar dimensions are valid and unbiased measures.
§5 uses LLM judges for qualitative metrics but reports no human agreement study or adversarial validation; the 44 empty-extractor re-scores in §6.1 also rely on judge consistency.
domain assumption OpenTelemetry/OpenInference spans capture every relevant model call, tool call, token count, and error across all nine harnesses.
§3.1-3.2; partially contradicted by crewai lacking token counts and by instrumented rows (tool_invocation, hallucination) that are set by the instrumentation rather than the agent (Figure 6).
domain assumption The matched configuration (DeepSeek-V4-pro FP4, same tool setup, step limit, timeout) makes remaining differences purely harness-level.
§6.1: adapters may still alter prompts, tool schemas, context management, or error handling, so measured differences may include adapter artifacts rather than pure harness behavior.
domain assumption Every harness-benchmark cell in the 9x23 grid executed validly and comparably.
§4.2 states 'Registry support does not imply that each framework-benchmark pair has passed end-to-end validation', yet Table 1 reports all 207 cells as scored runs.

invented entities (2)

Agent Task Protocol (ATP)
purpose: Internal software interface decoupling benchmark adapters from harness adapters.
§4.1: ATP is a new protocol introduced by this paper; no external adoption or independent specification is shown, and similar standards (CUBE, General Agent Evaluation) are cited as prior work.
Lifecycle-Aligned Evaluation taxonomy
purpose: Four-stage metric taxonomy organizing evaluation metrics by reasoning, action, answer, and runtime quality.
§5.1: a novel organizational scheme with no independent validation of its dimensions or their completeness.

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Cite this review

Pith. "Pith review of $A^2E$ : An End-to-End Agent Auditing Engine." pith.science (2026). https://pith.science/paper/FU6KBTTD

@misc{pith2026260807346,
  author       = {Pith},
  title        = {Pith review of: $A^2E$ : An End-to-End Agent Auditing Engine},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FU6KBTTD}},
  note         = {Machine review of arXiv:2608.07346}
}

abstract

With the rapid advancement of large language models (LLMs), harnesses have become essential infrastructure for deploying agents across a wide range of domains. The fast-evolving harness ecosystem has also made rigorous capability evaluation increasingly important. However, efficiently building an end-to-end, systematic, and comprehensive evaluation pipeline remains a significant challenge. To address this challenge, we introduce $A^2E$ (Agent Auditing Engine), an end-to-end evaluation engine designed for agent harnesses. $A^2E$ leverages our newly proposed Agent Task Protocol (ATP) to enable the rapid integration of evaluation tasks with different harnesses. Through an automatically instrumented Monitor, it captures and generates standardized execution traces during experiments. In the Evaluation stage, $A^2E$ systematically assesses harness capabilities using a suite of multidimensional metrics. Compared with correctness alone, these metrics provide a more fine-grained characterization of differences among harnesses in execution efficiency, tool use, task planning, and error recovery. Experiments conducted with $A^2E$ further reveal that model-harness combinations exhibit substantial performance variation across different types of tasks, and that no single combination consistently outperforms all others across every task. These findings not only demonstrate the necessity of systematic evaluation but also provide useful guidance for the co-evolving of models and harnesses. Our code is available at https://github.com/datamllab/A2E.

Figures

Figures reproduced from arXiv: 2608.07346 by the authors.

**Figure 1.** A2E provides end-to-end evaluation over an m × n benchmark–harness grid. (a) An Agent Task Protocol lets each of the 23 benchmarks be paired with each of the 9 agent frameworks, so no per-combination integration code is written. (b)Each petal shows, for one metric, the span from the worst to the best of the nine harnesses after averaging over the 23 benchmarks. Petals are grouped by Reasoning, Action, Answer, and Ru… view at source ↗

**Figure 2.** System overview. Task integrates benchmark management and execution support, Monitor provides unified agent access and instruments the runtime loop, and Evaluation performs multi-dimensional assessment with centralized result storage. 2.1 Framework Task Layer. The benchmark management block provides a unified mechanism for organizing diverse agent benchmarks. Benchmarks are maintained in a hierarchical benchmark tre… view at source ↗

**Figure 3.** Runtime workflow and data flow. The monitored task runner writes experiment runs and traces to [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

Figures from the paper (4 more)

**Figure 4.** Figure 4: task but their relationships place them within the wider execution process. The trace therefore records both the sequence of actions and their causal organization. Span-based tracing provides information that cannot be recovered from the final response alone. Span dura…

**Figure 5.** Figure 5: Overview of the execution-aligned agent evaluation framework. Process-level evaluation examines [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]

**Figure 6.** Figure 6: The 855 runs of the 19 non-sandbox benchmarks of Table [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]

**Figure 7.** Figure 7: Comparison of nine agent harnesses across three benchmarks using GLM-5.2 as the common API [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]

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Stephanie Lin, Jacob Hilton, and Owain Evans. Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958,

arXiv
[2023]

Agent planning benchmark: A diagnostic framework for planning capabilities in llm agents.arXiv preprint arXiv:2606.04874,

Haoyu Sun, Wenxuan Wang, Mingyang Song, Jujie He, Weinan Zhang, Yang Liu, Yang Yang, and Yu Cheng. Agent planning benchmark: A diagnostic framework for planning capabilities in llm agents.arXiv preprint arXiv:2606.04874,

arXiv
[2025]

Victor Barres, Ben Shi, Ola Zytek, Soham Ray, Keshav Dhandhania, and Pedram Razavi.τ 3-Bench: Advancing agent evaluation to knowledge and voice

URLhttps://arxiv.org/abs/2506.07982. Victor Barres, Ben Shi, Ola Zytek, Soham Ray, Keshav Dhandhania, and Pedram Razavi.τ 3-Bench: Advancing agent evaluation to knowledge and voice. Sierra,

arXiv
[2026]

Presented at the ICLR 2026 Workshop on Agents in the Wild

URL https://arxiv.org/abs/2602.22953. Presented at the ICLR 2026 Workshop on Agents in the Wild. Victor Barres, Honghua Dong, Soham Ray, Xujie Si, and Karthik Narasimhan.τ 2-bench: Evaluating conversational agents in a dual-control environment,

2026 arXiv

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.

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arXiv

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Swe-bench pro: Can ai agents solve long-horizon software engineering tasks?arXiv preprint arXiv:2509.16941,

Xiang Deng, Jeff Da, Edwin Pan, Yannis Yiming He, Charles Ide, Kanak Garg, Niklas Lauffer, Andrew Park, Nitin Pasari, Chetan Rane, et al. Swe-bench pro: Can ai agents solve long-horizon software engineering tasks?arXiv preprint arXiv:2509.16941,

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Exploration of llm multi-agent application implementation based on langgraph+ crewai.arXiv preprint arXiv:2411.18241,

Zhihua Duan and Jialin Wang. Exploration of llm multi-agent application implementation based on langgraph+ crewai.arXiv preprint arXiv:2411.18241,

arXiv

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AgentQuest: A modular benchmark framework to measure progress and improve LLM agents

Luca Gioacchini, Giuseppe Siracusano, Davide Sanvito, Kiril Gashteovski, David Friede, Roberto Bifulco, and Carolin Lawrence. AgentQuest: A modular benchmark framework to measure progress and improve LLM agents. InProceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies ...

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doi: 10.18653/v1/ 2024.naacl-demo.19

Association for Computational Linguistics. doi: 10.18653/v1/ 2024.naacl-demo.19. URLhttps://aclanthology.org/2024.naacl-demo.19/. Pengfei He, Zhenwei Dai, Bing He, Hui Liu, Xianfeng Tang, Hanqing Lu, Juanhui Li, Jiayuan Ding, Subhabrata Mukherjee, Suhang Wang, Yue Xing, Jiliang Tang, and Benoit Dumoulin. Traject-bench: A trajectory-aware benchmark for eva...

arXiv 2024

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Measuring massive multitask language understanding.arXiv preprint arXiv:2009.03300,

Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. Measuring massive multitask language understanding.arXiv preprint arXiv:2009.03300,

arXiv 2009

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Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, et al

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doi: 10.52202/079017-2365. URL https://proceedings.neurips.cc/paper_files/paper/2024/hash/ 877b40688e330a0e2a3fc24084208dfa-Abstract-Datasets_and_Benchmarks_Track.html. Adyasha Maharana, Dong-Ho Lee, Sergey Tulyakov, Mohit Bansal, Francesco Barbieri, and Yuwei Fang. Evaluating very long-term conversational memory of llm agents. InProceedings of the 62nd A...

doi:10.52202/079017-2365 2024