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Academic Research Scan — 2026-02-15

2026-02-15

Academic Research Scan — 2026-02-15

Sources: arXiv (4 queries), NBER RSS, Semantic Scholar (rate-limited), SSRN (blocked) Scan time: ~10:50 AM ET, Sunday Feb 15

🔬 High Priority Papers

AI Agents in Economic Environments

  • EcoGym: Evaluating LLMs for Long-Horizon Plan-and-Execute in Interactive Economies — (Multi-institution)

    • Benchmark for continuous LLM decision-making in interactive economies: Vending, Freelance, and Operation environments. 1000+ step horizons, business-relevant outcomes (net worth, income, DAU). Tests 11 leading LLMs. Finds no single model dominates; systematic tension between high-level strategy and efficient execution.
    • Link: https://arxiv.org/abs/2602.09514
    • Why it matters: First rigorous benchmark for AI agents operating autonomously in economic simulations — directly relevant to agentic commerce viability.

  • Autonomous Market Intelligence: Agentic AI Nowcasting Predicts Stock Returns — Zefeng Chen, Darcy Pu

    • Fully agentic LLM that autonomously searches the web and synthesizes information for daily stock predictions on Russell 1000. 100% agentic — no curated inputs. Top-20 picks yield 18.4 bps daily Fama-French alpha, annualized Sharpe ratio of 2.43. But ability is asymmetric: strong for identifying winners, weak for losers.
    • Link: https://arxiv.org/abs/2601.11958
    • Categories: q-fin.GN, q-fin.PM, q-fin.TR
    • Why it matters: First documented evidence of a fully autonomous AI agent with genuine stock-selection ability. The asymmetry finding (positive news = coherent signal, negative = noise) is a fundamental insight about AI agent information processing.

  • MERIT Feedback Elicits Better Bargaining in LLM Negotiators — Oh, Aghazada, Shin, Yun, Kim

    • AgoraBench: 9 bargaining settings (deception, monopoly, etc.) for LLMs. Human-aligned, economically-grounded metrics from utility theory. Finds baseline LLM strategies often diverge from human preferences; their mechanism improves negotiation with deeper strategic behavior.
    • Link: https://arxiv.org/abs/2602.10467
    • Why it matters: LLM agents negotiating commercial deals is a core use case for agentic commerce. This benchmark and training pipeline directly addresses LLM bargaining competence.

  • Would a Large Language Model Pay Extra for a View? Inferring Willingness to Pay from Subjective Choices — Reusens, Goethals, Calders, Martens

    • Studies LLM decision-making as purchasing assistants using multinomial logit models. Derives willingness-to-pay (WTP) estimates and compares to human benchmarks. LLMs systematically overestimate human WTP, especially with business personas.
    • Link: https://arxiv.org/abs/2602.09802
    • Why it matters: When AI agents spend money on behalf of humans, WTP calibration is critical. This paper is directly about the economics of AI agent purchasing decisions.

Multi-Agent Markets & Mechanism Design

  • Manipulation in Prediction Markets: An Agent-based Modeling Experiment — Smart, Mark, Bastian, Waugh

    • Agent-based simulation of prediction market manipulation by "whale" agents with biased valuations. Finds whales can temporarily shift prices proportional to capital share; herding behavior amplifies distortion. Proposes theoretical price-dynamics model.
    • Link: https://arxiv.org/abs/2601.20452
    • Categories: econ.GN, physics.soc-ph, q-fin.TR
    • Why it matters: As AI agents enter prediction markets (already happening with Polymarket bots), understanding manipulation dynamics is essential for market integrity.

  • Resisting Manipulative Bots in Meme Coin Copy Trading: A Multi-Agent Approach — Luo, Feng, Xu, Liu (WWW'26)

    • Multi-agent LLM system to defend against bot manipulation in meme coin copy trading. Uses chain-of-thought reasoning. Achieves 3% avg return per meme coin under realistic frictions. Published at ACM Web Conference 2026.
    • Link: https://arxiv.org/abs/2601.08641
    • Why it matters: Peer-reviewed work on AI agents operating in adversarial crypto markets. Directly about autonomous agent defense in real market conditions.

  • Who Restores the Peg? A Mean-Field Game Approach to Stablecoin Market Dynamics — Mohanty, Krishnamachari (USC)

    • Mean-field game framework for USDC/USDT de-pegging events. Models arbitrageurs and retail traders across primary (mint/redeem) and secondary (exchange) markets. Identifies non-linear breakdown threshold for peg recovery.
    • Link: https://arxiv.org/abs/2601.18991
    • Categories: q-fin.TR, cs.GT, econ.GN
    • Why it matters: Stablecoin stability is infrastructure for agentic commerce. This paper models the agent dynamics that determine whether payment rails hold.

  • Equity by Design: Fairness-Driven Recommendation in Heterogeneous Two-Sided Markets — Seputis, Timans, Verma

    • Formalizes two-sided fairness for multi-item recommendations. Uses CVaR for consumer utility fairness. Finds moderate fairness constraints can improve business metrics by diversifying exposure. FPTAS solvers make it practical at scale.
    • Link: https://arxiv.org/abs/2602.10739
    • Categories: cs.GT, cs.IR
    • Why it matters: Platform marketplace design insight — fairness as a tool for marketplace health, not just a cost. Directly applicable to AI agent marketplace design.

Multi-Agent Game Theory & Coordination

  • Towards Sustainable Investment Policies Informed by Opponent Shaping — Duque, Ciuca, Echchahed, Larochelle, Courville (Mila/ICLR 2026)

    • Multi-agent simulation (InvestESG) of investors and companies under climate risk. Applies Advantage Alignment (opponent shaping) to push agents toward socially beneficial equilibria. Accepted at ICLR 2026.
    • Link: https://arxiv.org/abs/2602.11829
    • Categories: cs.LG, cs.GT
    • Why it matters: Demonstrates that opponent shaping can align market incentives with collective welfare — a key mechanism for governing AI agent economies. Top venue (ICLR), top researchers (Larochelle, Courville).

  • Convex Markov Games and Beyond: Nash Equilibria — Barakat, Panageas, Varvitsiotis (AISTATS 2026)

    • Extends convex Markov games theory. Proves Nash equilibria = fixed points of projected pseudo-gradient dynamics. First analysis of common-interest settings. Policy gradient algorithm with sample complexity bounds.
    • Link: https://arxiv.org/abs/2602.12181
    • Categories: cs.GT, cs.LG, cs.MA
    • Why it matters: Foundational theory for multi-agent market equilibria. Needed for proving properties of AI agent market interactions.

  • Bandit Learning in Matching Markets with Interviews — Mirfakhar, Wang, Xu, Beyhaghi, Hajiesmaili

    • Models matching markets where both sides have uncertain preferences. Introduces strategic deferral (choosing not to hire). Achieves time-independent regret bounds — major improvement over O(log T).
    • Link: https://arxiv.org/abs/2602.12224
    • Categories: cs.GT, cs.AI, econ.TH
    • Why it matters: Matching markets are the backbone of gig economies and agent task marketplaces. This learning framework is directly applicable.

📄 Notable Papers

  • Behavioral Consistency Validation for LLM Agents: Stock-Market Simulation — Li et al.

    • Tests whether LLM agents' trading-style switching (fundamental vs. technical) aligns with behavioral finance theory. Year-long simulations with daily data. Finds only partial consistency, highlighting gaps in LLM market behavior.
    • Link: https://arxiv.org/abs/2602.07023
    • Categories: q-fin.TR, cs.AI

  • Seeing the Goal, Missing the Truth: Human Accountability for AI Bias — Cao, Jiang, Xu

    • Shows LLMs produce biased financial measurements when told the downstream use ("purpose leakage"). Goal-aware prompting improves pre-cutoff performance but not post-cutoff. AI bias here is a research design issue, not algorithmic.
    • Link: https://arxiv.org/abs/2602.09504
    • Categories: q-fin.GN, cs.AI

  • Trade-R1: Bridging Verifiable Rewards to Stochastic Environments — Sun et al.

    • RL framework for LLM financial decision-making. Uses RAG-based triangular consistency (evidence × reasoning × decision) to filter noisy market rewards. Reduces reward hacking with cross-market generalization.
    • Link: https://arxiv.org/abs/2601.03948
    • Categories: cs.AI, q-fin.TR

  • LLM-Based Multi-Agent Investment System for Chinese Public REITs — Li

    • Multi-agent LLM trading framework: 4 analyst agents + prediction agent + decision agent. Compares DeepSeek-R1 vs fine-tuned Qwen3-8B. Both outperform buy-and-hold in backtest. Small model matches large model in some scenarios.
    • Link: https://arxiv.org/abs/2602.00082
    • Categories: q-fin.ST, cs.AI, q-fin.TR

  • CM2: Reinforcement Learning with Checklist Rewards for Multi-Turn Agentic Tool Use — Zhang et al.

    • RL framework for training agentic tool-use: checklist rewards replace verifiable outcome rewards. 8B model matches judging model performance. Scalable recipe for multi-turn agents.
    • Link: https://arxiv.org/abs/2602.12268
    • Category: cs.AI

  • MalTool: Malicious Tool Attacks on LLM Agents — Hu, Jia, Li, Song, Gong (Duke/Berkeley)

    • First systematic study of malicious tool code in agent ecosystems. Taxonomy based on CIA triad. Generates 1,200 standalone + 5,287 embedded malicious tools. Existing detection (including VirusTotal) fails. Major security concern for agent marketplaces.
    • Link: https://arxiv.org/abs/2602.12194
    • Category: cs.CR

  • Adjusted Winner: from Splitting to Selling — Bredereck, Sun, Briman, Talmon

    • Extends the Adjusted Winner fair division method to allow selling resources under budget constraints. FPTAS algorithm for intractability. Relevant to agent resource allocation.
    • Link: https://arxiv.org/abs/2602.12231
    • Category: cs.GT

  • When Visibility Outpaces Verification: Agentic AI Discourse — Shi, DiFranzo

    • Analyzes r/OpenClaw and r/Moltbook Reddit communities for verification dynamics in agentic AI discussions. Finds "Popularity Paradox": high-visibility threads have delayed verification. Proposes "epistemic friction" design interventions.
    • Link: https://arxiv.org/abs/2602.11412
    • Categories: cs.CY, cs.AI, cs.HC

  • Blind Gods and Broken Screens: A Secure Agent Operating System (Aura) — Zou et al.

    • Proposes Aura: clean-slate secure agent OS replacing GUI scraping with structured agent-native interaction. Hub-and-Spoke topology, cryptographic identity, semantic firewall. 94.3% task success, 4.4% attack success. Major agent infrastructure paper.
    • Link: https://arxiv.org/abs/2602.10915
    • Categories: cs.CR, cs.AI

  • A Human-Centric Framework for Data Attribution in LLMs — Wührl, Ruckdeschel, Lo, Rogers

    • Framework for LLM data attribution as part of the data economy. Bridges NLP attribution methods with governance and creator economic incentives. Domain-specific negotiation between creators, users, platforms.
    • Link: https://arxiv.org/abs/2602.10995
    • Category: cs.CY

📊 Working Papers & Reports

NBER

  • No AI/agent-specific papers this week. Current NBER batch focuses on: tort reform (w34764), protests & redistribution (w34787), immigration economics (w34788-w34794), work-from-home (w34795), financial network connectedness (w34796), monetary policy (w34798), class mobility (w34800).
  • Source: https://www.nber.org/new.html

SSRN

  • Blocked by Cloudflare. SSRN search returned 403. Will retry with browser automation in future scan.

Semantic Scholar

🏛️ Institutions & Labs to Watch

  • Mila (Montréal) — Larochelle & Courville on multi-agent investment/opponent shaping (ICLR 2026). Consistent output on multi-agent coordination.
  • USC (Krishnamachari lab) — Stablecoin mean-field games. Active in DeFi mechanism design.
  • Duke/Berkeley (Song, Gong) — Agent security (MalTool). Critical for marketplace trust infrastructure.
  • KAIST (Se-Young Yun's group) — LLM bargaining/negotiation (MERIT). Building the benchmarks for agent commerce.
  • Multi-institution EcoGym team — Creating the definitive benchmark for LLM agents in economic environments.

📝 Scan Notes

Source Availability

  • arXiv: ✅ All 4 queries returned successfully. Rich results, especially in q-fin and cs.GT.
  • NBER: ✅ RSS feed parsed. No AI-relevant papers this week.
  • SSRN: ❌ Cloudflare 403. Needs browser-based access or API.
  • Semantic Scholar: ❌ Rate-limited (429). Need API key for reliable access.
  • Brave Search: ❌ Not configured. Would help for supplementary discovery.

Key Themes This Week

  1. LLM agents as economic actors — Multiple papers (EcoGym, WTP, MERIT) benchmarking LLMs in economic decision-making. The field is rapidly building evaluation infrastructure.
  2. Autonomous financial agents — The "Autonomous Market Intelligence" paper (Sharpe 2.43) is a landmark result for agentic finance.
  3. Agent security — MalTool and Aura both highlight that agent marketplace security is an urgent unsolved problem.
  4. Multi-agent game theory — ICLR/AISTATS papers advancing the mathematical foundations needed for AI agent markets.
  5. Crypto/DeFi agent dynamics — Stablecoin peg restoration, meme coin defense, and prediction market manipulation all involve agent-based modeling.

Suggestions for Next Scan

  • Apply for Semantic Scholar API key for reliable access
  • Set up Brave Search API for supplementary web search
  • Try SSRN via browser automation (openclaw profile)
  • Add Google Scholar alerts monitoring (needs workaround for auth)
  • Track specific authors: Krishnamachari, Se-Young Yun, Dawn Song (recurring in relevant work)