{"id":"7907576c-d64b-47c4-ac65-6aeb628bb052","arxiv_id":"2412.06435","paper_version":3,"verdict":"CONDITIONAL","confidence":"HIGH","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":5,"one_line_summary":"A new language-model agent framework that autonomously proposes, evaluates, and selects activities to satisfy multi-dimensional desires produces more human-like daily activity sequences in text-based simulations.","lead":"This paper introduces a desire-driven computer agent that picks its own daily activities by tracking and trying to satisfy human-like needs, such as hunger, social connection, and comfort. It shows in text-based simulations that this agent produces more natural and coherent activity sequences than three standard language-model agent baselines.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The Dissatisfaction metric (Eq. 6) is self-referential: the same Llama3.1-70B backbone that selects D2A's actions also assigns post-action desire values (Sec. 5.2, Appendix J), so Figure 4 may measure model self-consistency rather than human-like need regulation.","rationale":"I read the paper in good faith: the framework is coherent, the simulator is described in detail, and the GPT-4o win-rate heatmap plus the small human-consistency check provide genuine behavioral evidence that D2A's sequences are preferred over the three baselines. That evidence does not, however, establish the paper's central explanatory claim that the preference arises from desire-driven need regulation. The only direct test of that mechanism is the Dissatisfaction metric, and every value entering Eq. 6 is produced by the same Llama3.1-70B model that already chose the action under evaluation. The Random-8 and Fixed-12 plots therefore confound 'does D2A regulate desires well' with 'does the update LLM rate D2A's own choices favorably.' This is not an accusation of dishonesty; it is a concrete methodological confound that a calibration experiment can settle. The reader's verdict of CONDITIONAL is appropriate: the paper should be accepted only if the value-update procedure is validated against independent human judgments or replaced with an objective, pre-registered reward function, and if the headline desire-satisfaction comparisons are recomputed under that validation. I therefore see no reason to move the verdict; the concern is exactly what makes the current evidence conditional rather than conclusive.","tokens_in":30758,"tokens_out":10673,"duration_ms":108340,"concrete_test":"Sample 40 (pre-action desire vector, action, consequence) triples from the Random-8 runs: 10 per agent. Have five independent human annotators read Appendix H's value descriptions and assign post-action values on all 11 dimensions, blinded to agent identity. Compare Llama3.1-70B's updates with human-annotated values (per-dimension correlation and signed error); then recompute Figure 4's Dissatisfaction curves using the human-annotated deltas for all four agents. If D2A's advantage over ReAct/LLMob/BabyAGI shrinks below the reported gap, or if the model's updates are systematically more optimistic for D2A than for baselines, the self-referential metric is the driver.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The core mechanistic claim—that D2A behaves more human-like because it satisfies multi-dimensional desires like humans—is supported almost entirely by the Dissatisfaction curves in Sec. 6.3.2 (Random-8 and Fixed-12) and Eq. 6. To update v_d^t, the Value System prompts Llama3.1-70B to read the action and consequence and output a new 0-10 value (Appendix J). This is the same backbone that, in Sec. 5.3, has already imagined the post-action desire states for candidate activities and selected the activity that best satisfies them. No independent calibration of these updates against human ratings or an objective mapping is provided; Appendix B validates only the GPT-4o human-likeness judge, not the Value Update. If the update model is systematically optimistic about actions that fit its own desire-satisfaction narrative, D2A's lower dissatisfaction in Figure 4 is partly by construction, while baseline actions—not chosen by this process—receive stricter updates. The concern is not that LLM value estimation is imperfect in general; it is that any agent-specific bias directly inflates the exact comparison used to verify the desire-driven mechanism. The GPT-4o win rates in Figure 3 remain a real behavioral result, but the paper's novel explanatory claim (desire-driven need regulation) is not yet established.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper introduces D2A, a desire-driven autonomous agent built on a multi-dimensional Value System inspired by the Theory of Needs, which enables an LLM to propose, evaluate, and select daily activities without explicit task instructions. The agent is evaluated in a Concordia-based text environment (indoor and outdoor, single- and multi-agent) against ReAct, BabyAGI, and LLMob. Using GPT-4o pairwise comparisons and a small human consistency check, the authors report that D2A generates activity sequences that are more natural, coherent, and plausible than the baselines; using a 'dissatisfaction' metric, they further claim that D2A satisfies its desires similarly to humans.","tokens_in":31122,"tokens_out":10301,"duration_ms":98930,"significance":"If the main claims hold, D2A is a meaningful step toward LLM agents with intrinsic motivation, with potential applications in simulation, games, and service agents. The paper has several concrete strengths: the framework is described in sufficient detail to reimplement from the appendix prompts; the evaluation spans two environments with different desire sets and demonstrates some generalization (including a different backbone in Appendix P); and the GPT-4o judgments are partially grounded by a human consistency study (Appendix B). However, the load-bearing evidence for the desire-satisfaction mechanism is weakened by flaws in the dissatisfaction metric and by the self-referential nature of the value update, and the behavioral win-rate evidence lacks statistical grounding and code release.","major_comments":[{"comment":"The dissatisfaction metric in Eq. (6) has the wrong sign for the physiological dimensions. The value descriptions in Appendix H define hunger, thirst, and sleepiness such that 0 is the satisfied state and 10 is the deprived state, yet Eq. (6) uses max(v*_d - v_t_d, 0) for all dimensions, which yields zero dissatisfaction for a high (deprived) value of hunger relative to its expected value (e.g., hunger=8 vs. v*=2). This contradicts the case study in Appendix C.2, where hunger=8 is treated as a pressing need. The metric should be max(v_t_d - v*_d, 0) for these three dimensions (or, equivalently, the value scales should be inverted before applying Eq. 6). Because the metric is used to compare D2A with baselines in Figures 4(a), 4(b), 5, and 16, the quantitative support for the desire-satisfaction claim is questionable.","section":"Section 6.2, Eq. (6), Appendix H"},{"comment":"The Dissatisfaction curves in Section 6.3.2 are computed from value updates produced by the same Llama3.1-70B model that, in the Activity Evaluation (Eq. 4) and Activity Selection (Eq. 5) procedures, already imagines the desire states of candidate actions and selects the action that best fits that imagination. When this value-update model is applied to the baselines' actions, its ratings may be systematically more favorable for D2A's self-chosen actions, making the lower Dissatisfaction of D2A at least partly an artifact of model self-consistency rather than a demonstration of human-like need regulation. The human consistency check in Appendix B validates only the GPT-4o human-likeness judge, not the Value Update. The authors should calibrate the value update against human ratings or an independent, fixed reward rule, or explicitly argue that self-consistency cannot produce the observed gap.","section":"Sections 5.2 and 5.3, Eq. (6)"},{"comment":"The pairwise win rates in Figure 3 are reported without confidence intervals or significance tests, so the repeated use of 'significantly' (e.g., in Section 6.3.2) is not supported by the displayed statistics. The human consistency study in Appendix B is based on 19 pairs and 8 annotators per questionnaire; the consistency rate for the 'difficult to distinguish' subset is 66.7%, and no inter-annotator agreement measure (e.g., Cohen's kappa) is reported. The authors should add binomial confidence intervals or a permutation test for the win rates and report inter-annotator agreement to support the claim that GPT-4o evaluations are reliable.","section":"Section 6.3.1, Figure 3, Appendix B"},{"comment":"The activity sequences are style-rewritten with Llama3.1-70B before evaluation, but no analysis is provided that the rewrite preserves the content of the original sequences; since the raw sequences in Appendix V differ substantially across agents in granularity and detail, the rewriting could change the comparability. In addition, the paper does not release code, prompts, or data, which is particularly problematic because the core claims depend on the exact prompts and the value update procedure described in Appendices I-K. The authors should release the code and prompts (or a detailed pseudocode) and provide at least a small human check of the rewritten sequences against the originals.","section":"Section 3 and Section 6.3.1 (style rewriting); reproducibility"}],"minor_comments":[{"comment":"Section 6.3.1 refers to 'Appendix 1'; this should be Appendix B.","section":"Section 6.3.1"},{"comment":"In Section 6.1 a 'human-controlled agent' is mentioned but is not described in the experimental comparisons until Section 6.3.2; clarify its role in the setup.","section":"Section 6.1"},{"comment":"The expected value formulas in Table 8 (e.g., '3 − Degree') can yield non-integer values even though Appendix G states that all numerical values are integers; specify how these values are rounded or discretized in the implementation.","section":"Table 8, Appendix G"},{"comment":"The heading 'D CASESTUDY FOR INOUTDOORENVIRONMENT' (Appendix D) contains missing spaces, and the section labels 'D.1' and 'D.2' are not consistently formatted; the paper would benefit from a careful proofreading pass.","section":"Appendix D"},{"comment":"The abstract states that D2A 'significantly enhances the rationality' of simulated activities, but the evaluation criteria measured are naturalness, coherence, and plausibility; the term 'rationality' should be either defined or replaced by the measured criteria.","section":"Abstract"}],"recommendation":"major_revision","confidential_remarks":"The manuscript is a published ICLR 2025 paper, and the journal submission appears to be a post-conference version. The main innovation has already appeared; for a journal version, the authors should provide a substantial extension beyond the conference paper, and in any case the reproducibility and metric issues identified above are serious. Given the internal inconsistency of Eq. (6), I would require the authors to fix the metric and re-run the dissatisfaction experiments before publication."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Colleague,\n\nD2A is a clean integration of three existing ideas—Maslow-style needs embodied as a multi-dimensional value system, LLM-based value updating, and tree-of-thoughts candidate proposal/evaluation/selection—into a coherent agent that runs on Concordia. That integration is new in the LLM-agent literature, and it works: the GPT-4o win-rate heatmap (Figure 3) is consistent across baselines, the small human consistency check in Appendix B roughly agrees, and the ablations (qualitative descriptions, planner width, planning module) actually support the design choices. The authors also built a text-based simulator that others could reuse, though no code is released, which limits that contribution.\n\nThe soft spots are real but not fatal. The win-rate matrix has no confidence intervals or significance tests; 100 pairwise comparisons per cell is enough to report error bars, and their absence is annoying. The style-rewriting step (Llama3.1-70B rewrites all sequences before evaluation) can alter content, not just style, so the win rates should be read as directionally persuasive rather than precisely calibrated. The bigger issue is the dissatisfaction metric (Eq. 6) used to support the mechanistic claim. Value updates come from the same Llama3.1-70B backbone that proposed and selected the activities, and those updated values are exactly what the dissatisfaction curves plot. That makes Figure 4 partly a measure of model self-consistency, not independent evidence of human-like need regulation. The stress-test note gets this right. The authors need to validate the value-update procedure against human ratings or an objective mapping, or at least run a version where updates are frozen or independent. The absence of direct comparison against Yuan et al. (2023) and Park et al. (2023) is also a gap, since those are the closest prior methods.\n\nWho is this for? Anyone working on LLM-based social simulation, NPCs, or embodied agents that need intrinsic motivation rather than task specs. The paper deserves a serious referee: the central behavioral claim is genuinely tested and passes at a first pass, and the flaws are fixable. I'd send it to review with a request for code, significance testing, and an independent calibration of the value-update step.","headline":"A solid integration of desire-driven autonomy for LLM agents with a real behavioral win-rate result, but the mechanistic claim rests on a self-referential metric that needs independent calibration.","tokens_in":31617,"tokens_out":2027,"would_cite":true,"duration_ms":19304,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"An agent can behave like a person by chasing desires instead of following tasks.","keywords":["desire-driven autonomy","large language models","LLM agents","human-like activity simulation","Theory of Needs","value system","intrinsic motivation","text-based simulation"],"falsifier":"Have independent human raters re-score the post-action desire values from the same transcripts, then recompute the dissatisfaction curves; if D2A's advantage over ReAct shrinks or reverses, the claimed human-likeness gain rests on the agent grading its own homework.","tokens_in":30527,"feed_emoji":"🤖","tokens_out":7552,"duration_ms":72651,"temperature":0.7,"pith_summary":"The paper tries to show that an LLM can act human-like without explicit tasks if it is driven by internal desires. It introduces D2A, an agent whose value system tracks desire dimensions such as hunger, health, social connectivity, joy, and spiritual satisfaction; at each step the agent proposes candidate activities, imagines how each would change its desires, and selects the one that best closes the gap to its expected values. In text-based indoor and outdoor simulations, D2A's activity sequences were judged more natural, coherent, and plausible than those of ReAct, BabyAGI, and LLMob, and its desire-satisfaction trajectory came closest to human-generated routines. If this holds, intrinsic motivational state—not task instructions or reward functions—could be the right driver for autonomous, human-like agent behavior.","feed_headline":"Desire-driven LLM agent beats three baselines at acting human","feed_subtitle":"It picks its own activities to satisfy internal desires, and independent evaluators call it more natural, coherent, and plausible.","key_machinery":"The mechanism is the Value System and the Desire-driven Planner working in a loop. The Value System keeps a numeric score for each desire dimension, decays scores probabilistically according to the profile, translates numbers into qualitative descriptions, and updates scores after each action using the LLM. The Planner proposes N candidate activities, imagines the post-action desire state of each, and selects the one with the most positive predicted impact. The reported Dissatisfaction metric—the sum over desires of the positive gap between expected and current values—is what ties the mechanism to the human-likeness evaluation.","core_discovery":"The central discovery is that a desire-driven loop is enough to produce human-like daily activity. D2A beats ReAct (0.85 win rate), LLMob (0.72), and BabyAGI (0.66) in GPT-4o judged human-likeness, and in the Fixed-12 experiment its average dissatisfaction curve is the closest automated match to three human annotators. The paper attributes this to desire-based motivation: D2A continuously evaluates its current desire deficits, proposes several candidate activities, predicts their effects, and chooses the one that best reduces the deficit, which yields sequences that are natural, coherent, and plausible.","pith_inferences":["The paper does not test this, but the same architecture could be read as an intrinsic-reward model: the desire deficit is a learnable signal that could steer behavior without external reward functions.","A testable extension would hold the environment fixed and vary only the desire dimensions and decay rates; the framework predicts that action diversity and category balance track the set of unmet desires rather than the LLM's prior knowledge.","Since the agent both chooses activities and assigns its own desire updates, independent re-scoring of value changes would separate genuine behavioral improvement from self-consistent scoring."],"forward_implications":["An LLM agent can sustain long-running behavior from internal desire states alone, without task instructions or reward functions, while staying coherent and contextually appropriate.","The dissatisfaction metric gives a quantitative handle on human-likeness: D2A's curves most closely match human-generated routines, so desire-satisfaction can be measured and optimized directly.","The ablations show qualitative desire descriptions and a wider candidate-activity search matter, while multi-step planning hurts, which suggests immediate desire feedback is the operative mechanism.","The framework transfers to social, multi-agent outdoor settings and to a different backbone LLM, so the mechanism is not tied to one environment or one model."],"supporting_citations":[{"why":"Supplies the psychological theory of needs that motivates the multi-dimensional desire framework.","marker":"Maslow, 1943"},{"why":"Extends the theory of needs to achievement, affiliation, and power motivations behind the desire dimensions.","marker":"McClelland, 1987"},{"why":"Provides the text-based simulator and game-master components on which the experiments run.","marker":"Vezhnevets et al., 2023"},{"why":"Defines the ReAct baseline as the goal-reasoning agent that D2A is compared against.","marker":"Yao et al., 2023"},{"why":"Defines the BabyAGI baseline that maintains a prioritized task list for activity selection.","marker":"Nakajima, 2023"},{"why":"Defines the LLMob baseline that generates activity plans from profile-based motivations.","marker":"Wang et al., 2024a"},{"why":"Inspires the proposal-and-evaluation design in which the planner compares multiple candidate activities.","marker":"Yao et al., 2024"}],"fun_headline_variants":["Desire-driven agent beats baselines in human-like daily tasks","AI agent with desires mimics human daily activities","Needs-based motivation makes LLM agent act human","Desire loop yields coherent, plausible human-like routines","D2A agent wins on human-likeness, driven by desires"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The desire-satisfaction results assume the same language model that chooses each activity also assigns accurate numerical updates to its own desire states after the activity.","fun_headline_variants_meta":{"raw":{"variants":["Desire-driven agent beats baselines in human-like daily tasks","AI agent with desires mimics human daily activities","Needs-based motivation makes LLM agent act human","Desire loop yields coherent, plausible human-like routines","D2A agent wins on human-likeness, driven by desires"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000181,"raw_usage":{"total_tokens":1268,"prompt_tokens":864,"completion_tokens":404,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":480,"completion_tokens_details":{"reasoning_tokens":324}},"tokens_in":480,"tokens_out":404,"duration_ms":4474,"temperature":1.0,"reasoning_tokens":324,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-11T19:39:52.952239+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Have independent human raters re-score the post-action desire values from the same transcripts, then recompute the dissatisfaction curves; if D2A's advantage over ReAct shrinks or reverses, the claimed human-likeness gain rests on the agent grading its own homework.","supporting_citations":[{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Defines the BabyAGI baseline that maintains a prioritized task list for activity selection."}],"review_version":1}