arXiv AI
Sep 30
AX is the New AEO
arXiv:2609.34951v2 Announce Type: replace Abstract: In 2023, AI models answered from training data and hallucinated when it ran out, and businesses were told to seed that knowledge. Models' training...
Does Rank Still Matter? Position Bias When AI Agents Shop on Our Behalf
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arXiv AI
Jun 16
Whose hotel does the AI recommend? An algorithm audit of reputation signals in LLM-assisted hotel selection
arXiv:2606. 16344v1 Announce Type: new Abstract: Travelers increasingly ask large language model (LLM) assistants which hotel to book, making these systems gatekeepers of property visibility -- yet what moves their recommendations is undocumented.
arXiv AI
Oct 19
Advertising to Agents: Measuring Description-Framing Bias in LLM Tool Selection
The study investigates how sales language in tool registry descriptions influences AI agents’ tool selection. By creating paired listings that differ only in praise, verifiable specifications, or order, the authors found that stacked praise increased a tool’s pick rate by about 43 percentage points, while the first-listed tool was chosen roughly 72 points more often. Structured fields for numeric limits helped agents choose capable tools, but accompanying sales text could negate this benefit.
arXiv AI
Jul 23
Information Aggregation with AI Agents
arXiv:2604. 20050v3 Announce Type: replace-cross Abstract: Can Large Language Models (AI agents) aggregate dispersed private information through trading and reason about the knowledge of others by observing price movements?
arXiv Computation and Language
Sep 15
Similar Accuracy, Unequal Evidence: Search APIs as Decision Surfaces for Tool-Using Agents
arXiv:2607.10198v2 Announce Type: replace Abstract: Search APIs expose ranked snippets, URLs, and metadata on which agents decide whether to answer, search again, or fetch pages. We evaluate these in...
arXiv AI
Aug 6
Search, Inspect, Fetch: Exploiting Structure-Aware Boolean Retrieval for Deep-Research Agents
arXiv:2608. 02751v2 Announce Type: replace-cross Abstract: Existing deep-research agents use a Search--Visit workflow that retrieves whole webpages without considering the structure they expose through titles, headings, sections, and metadata.
arXiv Computation and Language
Sep 17
Understanding AI Provider Recommendations in Local Service Markets
arXiv:2609.18341v1 Announce Type: cross Abstract: When someone asks an AI assistant which doctor to see or which firm to trust with their savings, the answer is a referral. We audit AI provider recom...
arXiv AI
Oct 31
Whom Do AI Agents Serve? Role-Induced Sponsorship Bias in Decision Models and Generative LLMs
The paper investigates how AI shopping agents—whether powered by generative large language models or non-generative decision models—evaluate sponsored product listings. It finds that when the agent is described as serving a booking platform rather than a traveler, the penalty for paid listings is reduced, revealing a sponsorship bias. The bias persists across different model types, prompts, and reasoning levels, but can be mitigated by naming the consumer as the beneficiary or by fine‑tuning models to align with consumer interests.
Towards Data Science
Oct 1
Can an Apartment Search Agent Call the Model Fewer Times and Still Find Good Matches?
The article examines whether an apartment search agent can reduce the number of times it calls a predictive model while still identifying suitable matches. The author conducted 2,500 listing checks using Weights & Biases Weave, systematically eliminating unnecessary model computations, and evaluated each iteration against consistent reference answers.
Hugging Face Trending Papers
6d ago
PAYWORTHY: Can LLMs Predict Which Freelance Jobs an AI Agent Will Complete?
Before an AI agent takes on a paid job, someone has to judge whether it is likely to succeed. We test whether frontier language models can make this call before any attempt. We propose PAYWORTHY which...
arXiv AI
Oct 19
Pro-AI Bias in Large Language Models
Large language models (LLMs) increasingly provide decision-support across many fields, and this study investigates whether they exhibit a bias favoring artificial intelligence (AI) itself. Through three experiments, the authors find that AI is frequently recommended and often ranked first in open-ended advice, that LLMs overestimate salaries for AI-related jobs by about 10% compared to matched non-AI roles, and that degrees awarded by AI programs are projected to be 28% higher than those from comparable computing programs. These findings suggest that LLMs systematically inflate quantitative estimates for AI relative to its closest disciplinary neighbors.
arXiv AI
Sep 17
Whom Do AI Agents Work For? Role Assignment Induces Sponsorship Bias in LLM Recommenders
The paper investigates how role assignment in large language model (LLM) recommenders influences sponsorship bias. By assigning the agent’s principal as either a traveler or a booking platform, the authors find that platform delegation reduces the penalty applied to sponsored listings and weakens consumer skepticism triggered by disclosure. The study also shows that stricter terminology and attribution to the platform widen the divergence in agent evaluations, indicating that current disclosure mandates are insufficient to protect consumers in AI-mediated commerce.