Position: Stop Acting Like Language Model Agents Are Normal Agents

Elija Perrier; Michael Timothy Bennett

REVIEW 3 major objections 6 minor 1 cited by

Position: Stop Acting Like Language Model Agents Are Normal Agents

T0 review · 3 major / 6 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read AI agents lack the stable identity real agency requires

desk verdict Worth reading as a well-structured position paper, but the categorical claim overreaches: the key step that scaffolding cannot stabilize LMA identity is an unproven conjecture, so the paper is a call to arms, not a settled result. read the letter →

arxiv 2502.10420 v1 pith:TQ6L4ECM submitted 2025-02-04 cs.AI cs.CL

Elija Perrier , Michael Timothy Bennett This is my paper · ORCID

classification cs.AIcs.CL

keywords languagemodelagentsagenticidentityLLMpathologiesontologicalstabilityscaffoldingagentevaluationstochasticitysemanticsensitivity

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 position paper argues that language-model agents (LMAs)—LLMs wrapped in memory, tools, and planning modules—should not be treated as ordinary agents with stable goals, boundaries, and identity. It claims that four intrinsic properties of the underlying LLM—statelessness, stochasticity, semantic sensitivity, and linguistic intermediation—destabilize the four ontological conditions that agency requires: identifiability, continuity, persistence, and consistency. Scaffolding can mask but not remove these pathologies, and the authors conjecture a necessary trade-off between a system's ontological stability and its generative power. The practical upshot is that LMAs' agentic properties must be measured before, during, and after deployment rather than assumed, because treating them as normal agents produces a false sense of utility and trustworthiness, especially in high-stakes settings.

What carries the argument

The analytic core is a pairing of four LLM pathologies with four ontological identity conditions. The pathologies are statelessness (no internal state transition between queries), stochasticity (sampling from token distributions), semantic sensitivity (small prompt changes producing large output changes), and linguistic intermediation (everything filtered through tokenized text). The identity conditions are identifiability (distinguishable from the environment and other agents), continuity (identity across time steps), persistence (identity across different instantiations and sessions), and consistency (non-contradictory description and action). The argument is that the pathologies violate or destabilize these conditions, and that scaffolding only relocates or masks the instability because it does not change the LLM's computation. A named conjecture completes the machinery: a necessary trade-off between ontological stability and power, analogous to bias-variance or no-free-lunch trade-offs.

What would settle it

A concrete falsifier: build an LMA with external memory, a stateful wrapper, and deterministic decoding, then run the same prompt sequence 10,000 times and adversarially perturb each prompt at the semantic level; if the system's outputs, goals, and self-description remain stable across trials and perturbations—showing measurable continuity, persistence, and consistency—while task performance matches or exceeds a baseline LLM, then the claim that scaffolding can only mask pathologies would be refuted.

Watch

Extended reading notes

Core claim

The paper's central claim is that LMAs are not agents in the normal sense, and that acting as if they are limits their usefulness. The mechanism is that four LLM pathologies—statelessness, stochasticity, semantic sensitivity, and linguistic intermediation—propagate upward and destabilize the ontological identity conditions (identifiability, continuity, persistence, consistency) that any agent must satisfy, regardless of which detailed theory of agency one adopts. Scaffolding such as external memory, tool use, planning modules, and infrastructure can improve surface coherence and provide audit trails, but because memory is only additional context ingested at query time and all action passes through text, the underlying pathologies remain. In response, the paper proposes agentic evaluations: mechanistic interpretability inside the model and agent-identity evaluations outside it, to quantify the degree to which a given LMA configuration preserves its ontology, so that residual instability is measured rather than assumed away.

Load-bearing premise

The load-bearing premise is the conjecture that scaffolding—memory, tools, planning, infrastructure—can mitigate but never cure the LLM pathologies, because any cure would require giving up the generative flexibility that makes LLMs powerful; if some scaffolding configuration fully stabilized an LMA's identity while preserving power, the paper's central conclusion would collapse.

Editorial extensions

If this is right

Developers should stop designing LMAs on the assumption of stable identity and instead instrument systems to detect drift, contradiction, and boundary blurring during operation.
High-stakes deployments in finance, healthcare, and government should not rely on the apparent agentic coherence of LMAs without quantitative, risk-based evaluations of their identity robustness.
Scaffolding configurations should be evaluated not only on task performance but also on how well they preserve identifiability, continuity, persistence, and consistency; a configuration that genuinely stabilizes identity would be a real advance rather than a patch.
The proposed trade-off predicts that pushing LMAs toward full determinism and ontological rigidity will reduce their generative breadth, giving designers a measurable constraint for future agent architectures.
Measuring agentic properties before, during, and after deployment becomes a standard assurance practice, comparable to auditing, for any system claimed to be agentic.

Reading between the lines

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

If the trade-off conjecture is right, one can search for a Pareto front between identity-stability metrics and task-performance metrics across scaffolding configurations, predicting a negative correlation that is not an artifact of engineering quality.
The identity-crisis framing suggests a concrete evaluation target: an agent-identity score built from repeated-query consistency, cross-instantiation persistence, and adversarial semantic-perturbation sensitivity, which regulators could use to compare systems on a common scale.
The same logic extends to multi-agent systems: when LMAs share memory stores, tools, or the same underlying model, boundary blurring makes it ambiguous which agent caused which action, so accountability frameworks may need to track causal identity, not just output traces.
If linguistic intermediation is the irreducible bottleneck, grounding LMAs in formal, non-linguistic action schemas (typed tool contracts) could reduce semantic sensitivity without sacrificing generative breadth; this is a testable direction the paper does not explore.

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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

Two things to know before you read this. First, the paper's useful core is the claim that LMA identity—identifiability, continuity, persistence, consistency—is a real problem and should be measured rather than assumed. That is a sensible, actionable message. Second, the paper's stronger claim that LMAs can never be normal agents is not actually argued. It rests on the Sec 5.3 conjecture that stabilizing scaffolding would destroy the generative power of LLMs, and that conjecture is asserted rather than defended.

What is new here is the packaging: the four LLM pathologies (statelessness, stochasticity, semantic sensitivity, linguistic intermediation) are each well-known and well-cited, but organizing them as threats to ontological identity conditions for agents is a useful framing. The paper also does a fair job of engaging with counterarguments in Sec 5, and it explicitly acknowledges that scaffolding improves things. That honesty counts for something.

Now the soft spots, in proportion. The gap you'll feel most is the move from 'the LLM is stateless' to 'the LMA is stateless.' The authors define an LMA as LLM plus scaffolding, and then in Sec 4.1 they say memory is 'just another form of context' and the LLM 'is never truly updated.' But a composite system with external memory does have state—the memory read and written between calls is part of the system. A classical agent also has a fixed transition function and mutable storage; nobody says it has no identity because its state is external to its transition function. The paper needs to argue why the composite state cannot anchor identity, and instead it offers an analogy to bias-variance that doesn't do the work. Deterministic systems can be universal, so determinism doesn't automatically impose a power cost. This is the load-bearing step, and it is unsupported.

The positive proposal—agent identity evaluations, measuring ontological robustness before/during/after deployment—is sensible but only sketched. No metrics, no methodology, no examples. For a position paper that might be okay, but it makes the paper more of a call to arms than a solution.

Overall: if you want a clear statement of the conceptual worries about treating LLM agents as normal agents, this is a decent read. It deserves peer review because the question matters and the paper is serious, but a referee should push for either a weaker conclusion or a real argument against scaffolding. I'd probably cite it as a position statement, but I wouldn't rely on its impossibility claim.

Referee Report

3 major / 6 minor

Summary. This Position paper argues that Language Model Agents (LMAs), defined as agentic systems built around LLMs and augmented with scaffolding such as memory, tools, planning modules, and infrastructure, should not be treated as normal agents. The authors identify four 'pathologies' intrinsic to LLMs: statelessness, stochasticity, semantic sensitivity, and linguistic intermediation. They argue that these pathologies destabilize the ontological identity conditions of agents—identifiability, continuity, persistence, and consistency—even when scaffolding is added. The paper concludes that scaffolding can at best mask these problems, conjecturing a necessary trade-off between ontological stability and generative power. The authors recommend that agentic identity be measured before, during, and after deployment rather than assumed.

Significance. If the central claim is accepted, the paper has significant implications for the design, governance, and trustworthiness of LMAs in high-stakes applications, potentially shifting the field away from treating LMAs as stable agents toward a more measurement-oriented approach. The paper's emphasis on operational evaluation of agentic identity is a constructive contribution, and its enumeration of LLM pathologies is a useful synthesis. However, the argument's categorical conclusion rests on an explicitly stated conjecture that is not proven, and the analysis does not fully address the composite nature of LMAs, which include external state. The paper is nonetheless a valuable position statement that identifies a genuine conceptual gap in current agentic AI discourse.

major comments (3)

[5.3] The paper's central conclusion that LMAs 'are not normal agents' (Sec 1.2) is load-bearing on the conjecture in Sec 5.3 that 'there is a necessary trade-off between the ontological stability of such systems on the one hand and their power on the other.' The only support offered is an analogy to no-free-lunch theorems and the bias-variance trade-off. This analogy does not establish the claim: deterministic systems can be universal (e.g., a deterministic Turing machine is Turing-complete), so determinism does not imply low expressive power. Moreover, the paper defines LMAs as including scaffolding (Sec 1, Sec 4), so the LMA is a composite system with external memory. The claim in Sec 4.1 that 'memory is just another form of context' identifies the agent with the LLM rather than the composite; a classical agent also has a fixed transition function and mutable storage, and its identity is anchored in the total state. The paper must either provide a proof or evidence for the trade-off, or restrict the claim to LLMs without scaffolding. As written, the categorical claim is not supported.
[Secs 3-4] The four pathologies in Sec 3 (statelessness, stochasticity, semantic sensitivity, linguistic intermediation) are properties of the bare LLM, but the LMA is defined as an 'agentic system based upon LLMs' including memory, tools, planning, and infrastructure (Sec 1, Sec 4). The paper does not show that these pathologies persist in the composite in a way that defeats identity. For example, stochasticity can be bounded by constrained decoding or repetition penalties, semantic sensitivity can be reduced by input validation and structured tool schemas (Sec 4.2), and statelessness is directly addressed by external memory (Sec 4.1). The paper's response in Sec 5.3 that scaffolding only 'masks' problems is asserted, not demonstrated. Additionally, the identity conditions in Sec 2.2 are introduced as matters of degree (Sec 2.1 acknowledges agency 'is a matter of degree rather than a binary property'), yet the argument applies them as if any residual instability invalidates agency. The paper should state the threshold of stability required for 'normal' agency and explain why current scaffolding fails it.
[Sec 2.1] The paper claims that 'regardless of which criteria of agency is chosen, LMAs struggle to satisfy it' (Sec 2.1), but it only analyzes its own four identity conditions (identifiability, continuity, persistence, consistency) and does not engage with alternative agency criteria such as behavioral or functionalist definitions, which might be satisfied by the composite system. The term 'normal agent' is never defined precisely; the argument oscillates between 'normal sense' (Sec 1.2), 'traditional agents' (Sec 3.1), and 'classical computational agents' (Sec 2.2.4). Without a precise target, the central claim is difficult to evaluate. The paper should either define 'normal agent' operationally or scope the claim to the specific identity conditions under discussion.

minor comments (6)

[Abstract] The phrase 'persistence and and consistency' contains a duplicated 'and'.
[Sec 3.1] The phrase 'plethora if different queries' should read 'plethora of different queries'.
[Sec 5.3] The phrase 'in analogous spirit with no free lunch theorems' should be 'in the spirit of no-free-lunch theorems'.
[References] Several bibliographic URLs are truncated and will not resolve, including the Docker blog entry ending in 'llm-docker-for-local-' and the Fortune article ending in 'nvidia's-jensen-huang -'.
[Sec 4.3] The sentence 'These characteristics of memory integration mean that proposed cognitive architectures such as COALA ... are problematised' is confusing because the preceding sentences discuss planning modules rather than memory integration; the referent of 'these characteristics' should be clarified.
[Sec 2.2.1] In item (i) of Identifiability, 'criteria by which to identity an agent' should be 'criteria by which to identify an agent'.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the paper's argument is a position argument built on cited empirical LLM properties and an explicitly labeled conjecture, not on a fitted or self-referential derivation.

full rationale

The paper is a position paper rather than a formal derivation: it defines LMAs as agentic systems based on LLMs, enumerates LLM properties (statelessness, stochasticity, semantic sensitivity, linguistic intermediation) as pathologies, and argues that scaffolding does not remove them. No equations, fitted parameters, or quantitative predictions are present, so the fitted-input-called-prediction pattern does not apply. The central inference from LLM pathologies to LMA identity instability is an architectural argument, not a definitional tautology: the paper explicitly considers scaffolding (memory, tools, planning, infrastructure) and gives reasons why each leaves the underlying LLM exposed to the pathologies. The key claim that scaffolding cannot fully cure the identity crisis is admitted in Sec 5.3 to be a conjecture ('we conjecture that there is a necessary trade-off between the ontological stability of such systems on the one hand and their power on the other'); an acknowledged conjecture is a limitation, not a circular step. Self-citations to Bennett supply framing concepts such as 'causal identities' and 'computational dualism,' but the core agentic criteria (identifiability, continuity, persistence, consistency) are standard and the argument does not reduce to those citations. Overall, the derivation chain is not circular; the paper's weaknesses are in the strength of its unsupported conjecture, which is a correctness or evidential concern, not a circularity concern.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The paper rests on several philosophical and empirical premises it does not prove: the necessity of identity conditions for agency, the characterization of LLMs as intrinsically pathological, and the conjecture that scaffolding cannot overcome these pathologies. No free parameters are fitted and no new entities are posited.

assumptions (4)

domain assumption At minimum, an agent must be distinguished from its environment and be capable of action, pursuing goals, and interacting responsively.
Invoked in Sec 2.1 as the baseline for agency; the paper uses this to argue LMAs struggle to satisfy any standard agency criteria.
domain assumption Identifiability, continuity, persistence, and consistency are necessary preconditions of agency.
Introduced in Sec 2.2 and used as the framework for the identity crisis; these are philosophical claims that are not universally accepted.
domain assumption LLMs are intrinsically stateless, stochastic, semantically sensitive, and linguistically intermediated.
Stated in Sec 3 as constitutive pathologies, supported by selected citations rather than systematic evidence.
ad hoc to paper There is a necessary trade-off between ontological stability and generative power in LLM-based systems.
Conjectured in Sec 5.3; load-bearing for the claim that scaffolding cannot cure the pathologies, but not demonstrated.

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Cite this review

Pith. "Pith review of Position: Stop Acting Like Language Model Agents Are Normal Agents." pith.science (2026). https://pith.science/paper/TQ6L4ECM

@misc{pith2026250210420,
  author       = {Pith},
  title        = {Pith review of: Position: Stop Acting Like Language Model Agents Are Normal Agents},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TQ6L4ECM}},
  note         = {Machine review of arXiv:2502.10420}
}

read the original abstract

Language Model Agents (LMAs) are increasingly treated as capable of autonomously navigating interactions with humans and tools. Their design and deployment tends to presume they are normal agents capable of sustaining coherent goals, adapting across contexts and acting with a measure of intentionality. These assumptions are critical to prospective use cases in industrial, social and governmental settings. But LMAs are not normal agents. They inherit the structural problems of the large language models (LLMs) around which they are built: hallucinations, jailbreaking, misalignment and unpredictability. In this Position paper we argue LMAs should not be treated as normal agents, because doing so leads to problems that undermine their utility and trustworthiness. We enumerate pathologies of agency intrinsic to LMAs. Despite scaffolding such as external memory and tools, they remain ontologically stateless, stochastic, semantically sensitive, and linguistically intermediated. These pathologies destabilise the ontological properties of LMAs including identifiability, continuity, persistence and and consistency, problematising their claim to agency. In response, we argue LMA ontological properties should be measured before, during and after deployment so that the negative effects of pathologies can be mitigated.

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

Agent Identity Evals: Measuring Agentic Identity
cs.AI 2025-07 conditional novelty 5.0 of 10

Introduces Agent Identity Evals (AIE), five similarity-based metrics for LMA identity stability, with pilot experiments showing identifiability always at zero and no statistical support.

Reference graph

Works this paper leans on

12 extracted references · 3 canonical work pages · cited by 1 Pith paper

[4]

URL https://doi.org/10.48550/arXiv.2305.14992

doi: 10.48550/arXiv.2305.14992. URL https://doi.org/10.48550/arXiv.2305.14992. Heaven, W . D. Sam altman says helpful agents are poised to become ai’s killer function. https://www.technologyreview.com/2024/05/01/1091979

arXiv doi:10.48550/arxiv.2305.14992 2024
[5]

Journal, W

URL https://doi.org/10.1007/978-94-007-1356-7_5 . Journal, W . S. Ai agents can do more than answer queries. that raises a few questions. W all Street Journal T echnology , 2024. URL https://www.wsj.com/articles/ai-agents-can-do-more- than-answer-queries-that-raises-a-few-ques Kapoor, S., Stroebl, B., Siegel, Z. S., Nadgir, N., and Narayanan, A. AI Agents...

arXiv 2024
[7]

Accessed: 2025-01-04. PwC. Agentic ai – the new frontier in genai. https://www.pwc.com/m1/en/publications/documents/20

work page 2025
[8]

Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al

Accessed: 2025-01-04. Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9, 2019. Rawte, V ., Chakraborty, S., Pathak, A., Sarkar, A., Ton- moy, S. M. T. I., Chadha, A., Sheth, A. P ., and Das, A. The troubling emergence of hallucination in large language model...

arXiv 2025
[9]

URL https://doi.org/10.48550/arXiv.2308.03427

doi: 10.48550/arXiv.2308.03427. URL https://doi.org/10.48550/arXiv.2308.03427. Russell, S. and Norvig, P .Artiﬁcial Intelligence: A Modern Approach. Prentice Hall, 3 edition, 2010. Russell, S. J. and Norvig, P . Artiﬁcial Intelligence: A Mod- ern Approach. 4 edition, 2021. Schick, T., Dwivedi-Y u, J., Dessi, R., Raileanu, R., Lomeli, M., Hambro, E., Zettl...

work page doi:10.48550/arxiv.2308.03427 2010
[10]

Waiwitlikhit, S., Stoica, I., Sun, Y ., Hashimoto, T., and Kang, D

Accessed: 2025-01-04. Waiwitlikhit, S., Stoica, I., Sun, Y ., Hashimoto, T., and Kang, D. Trustless audits without revealing data or mod- els. arXiv: 2404.04500 [cs.CR] , April 2024. URL http://arxiv.org/abs/2404.04500. Wang, H. e. Are large language models really ro- bust to word-level perturbations? September

arXiv 2025
[11]

URL http://arxiv.org/abs/2309.11166

doi: 10.48550/arXiv.2309.11166. URL http://arxiv.org/abs/2309.11166. arXiv:2309.11166. 14 Stop Acting Like Language Model Agents Are Normal Agents Wang, P . Rigid Flexibility: The Logic of Intelligence . Ap- plied Logic Series. Springer, 2006. Wang, W ., Dong, L., Cheng, H., Liu, X., Y an, X., Gao, J., and Wei, F. Augmenting Language Models with Long- Ter...

arXiv doi:10.48550/arxiv.2309.11166
[12]

URL https://doi.org/10.48550/arXiv.2306.12672

doi: 10.48550/arXiv.2306.12672. URL https://doi.org/10.48550/arXiv.2306.12672. Wooldridge, M. An Introduction to Multiagent Systems . John wiley & sons, 2009. Wooldridge, M. and Jennings, N. R. Intelligent agents: Theory and practice. The Knowledge Engineering Re- view, 10(2):115–152, 1995. Wu, Q., Bansal, G., Zhang, J., Wu, Y ., Li, B., Zhu, E., Jiang, L...

arXiv doi:10.48550/arxiv.2306.12672 2009

Show all 12 references

[2021]

ISBN 978- 1-4503-8309-7

Association for Computing Machinery. ISBN 978- 1-4503-8309-7. doi: 10.1145/3442188.3445922. URL https://doi.org/10.1145/3442188.3445922. event-place: Virtual Event, Canada. Bennett, M. T. Compression, the fermi paradox and artiﬁ- cial super-intelligence. In Artiﬁcial General I...
[2022]

URL https://doi.org/10.48550/arXiv.2212.08073

doi: 10.48550/arXiv.2212.08073. URL https://doi.org/10.48550/arXiv.2212.08073. Bender, E. M., Gebru, T., McMillan-Major, A., and Shmitchell, S. On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accounta...

doi arXiv
[2023]

URL https://doi.org/10.48550/arXiv.2305.11738

doi: 10.48550/arXiv.2305.11738. URL https://doi.org/10.48550/arXiv.2305.11738. Gu, X., Zheng, X., Pang, T., Du, C., Liu, Q., Wang, Y ., Jiang, J., and Lin, M. Agent smith: A single image can jailbreak one million multimodal llm agents expo- nentially fast. arXiv preprint arXiv...

doi arXiv
[2024]

Henighan, T., Carter, S., Hume, T., Elhage, N., Lasenby, R., Fort, S., Schiefer, N., and Olah, C

Accessed: 2025-01-04. Henighan, T., Carter, S., Hume, T., Elhage, N., Lasenby, R., Fort, S., Schiefer, N., and Olah, C. Superposition, memorization, and double descent. Transformer Circuits Thread, 6:24, 2023. Horowitz, A. Emerging architectures for llm applications, 2023. URL...

2025

Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.

[1] [4]

URL https://doi.org/10.48550/arXiv.2305.14992

doi: 10.48550/arXiv.2305.14992. URL https://doi.org/10.48550/arXiv.2305.14992. Heaven, W . D. Sam altman says helpful agents are poised to become ai’s killer function. https://www.technologyreview.com/2024/05/01/1091979

arXiv doi:10.48550/arxiv.2305.14992 2024

[2] [5]

Journal, W

URL https://doi.org/10.1007/978-94-007-1356-7_5 . Journal, W . S. Ai agents can do more than answer queries. that raises a few questions. W all Street Journal T echnology , 2024. URL https://www.wsj.com/articles/ai-agents-can-do-more- than-answer-queries-that-raises-a-few-ques Kapoor, S., Stroebl, B., Siegel, Z. S., Nadgir, N., and Narayanan, A. AI Agents...

arXiv 2024

[3] [7]

Accessed: 2025-01-04. PwC. Agentic ai – the new frontier in genai. https://www.pwc.com/m1/en/publications/documents/20

work page 2025

[4] [8]

Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al

Accessed: 2025-01-04. Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9, 2019. Rawte, V ., Chakraborty, S., Pathak, A., Sarkar, A., Ton- moy, S. M. T. I., Chadha, A., Sheth, A. P ., and Das, A. The troubling emergence of hallucination in large language model...

arXiv 2025

[5] [9]

URL https://doi.org/10.48550/arXiv.2308.03427

doi: 10.48550/arXiv.2308.03427. URL https://doi.org/10.48550/arXiv.2308.03427. Russell, S. and Norvig, P .Artiﬁcial Intelligence: A Modern Approach. Prentice Hall, 3 edition, 2010. Russell, S. J. and Norvig, P . Artiﬁcial Intelligence: A Mod- ern Approach. 4 edition, 2021. Schick, T., Dwivedi-Y u, J., Dessi, R., Raileanu, R., Lomeli, M., Hambro, E., Zettl...

work page doi:10.48550/arxiv.2308.03427 2010

[6] [10]

Waiwitlikhit, S., Stoica, I., Sun, Y ., Hashimoto, T., and Kang, D

Accessed: 2025-01-04. Waiwitlikhit, S., Stoica, I., Sun, Y ., Hashimoto, T., and Kang, D. Trustless audits without revealing data or mod- els. arXiv: 2404.04500 [cs.CR] , April 2024. URL http://arxiv.org/abs/2404.04500. Wang, H. e. Are large language models really ro- bust to word-level perturbations? September

arXiv 2025

[7] [11]

URL http://arxiv.org/abs/2309.11166

doi: 10.48550/arXiv.2309.11166. URL http://arxiv.org/abs/2309.11166. arXiv:2309.11166. 14 Stop Acting Like Language Model Agents Are Normal Agents Wang, P . Rigid Flexibility: The Logic of Intelligence . Ap- plied Logic Series. Springer, 2006. Wang, W ., Dong, L., Cheng, H., Liu, X., Y an, X., Gao, J., and Wei, F. Augmenting Language Models with Long- Ter...

arXiv doi:10.48550/arxiv.2309.11166

[8] [12]

URL https://doi.org/10.48550/arXiv.2306.12672

doi: 10.48550/arXiv.2306.12672. URL https://doi.org/10.48550/arXiv.2306.12672. Wooldridge, M. An Introduction to Multiagent Systems . John wiley & sons, 2009. Wooldridge, M. and Jennings, N. R. Intelligent agents: Theory and practice. The Knowledge Engineering Re- view, 10(2):115–152, 1995. Wu, Q., Bansal, G., Zhang, J., Wu, Y ., Li, B., Zhu, E., Jiang, L...

arXiv doi:10.48550/arxiv.2306.12672 2009