The paper investigates a behavioral mechanism for artificial partiality in conversational agents, defining partiality as caring (allocation of surplus interaction above a guaranteed floor) and particularity (per-user state estimates from relationship history). It implements these as a persistence layer over an open-weight base model and evaluates them in constructed multi-session relationships, finding that the mechanism reproduces the ordinal shape of a specified curve but not its magnitude, and that a guarded revision satisfies probe-quality equivalence on a calibrated battery but not on a second battery. The authors note that the mechanism is a candidate for future perception studies rather than evidence that users perceive love, and they outline ethical constraints such as a stranger-treatment floor, bounded surplus, disclosure, and de-intensification.
arXiv:2607. 15883v1 Announce Type: cross Abstract: Large language models are broadly capable, yet in sustained one-to-one conversation they still read as flat: competent, responsive, and somehow not quite the presence of a mind.
The study examines how users of a major dating platform respond to autonomous LLM agents that converse on their behalf. Using two large surveys, researchers built a latent-variable model showing that willingness to send and receive agent-mediated messages are highly correlated yet distinct. The findings reveal a delegation asymmetry: users are more willing to deploy their own agent than to engage with others’ agents, leading to low overall reciprocity and gender‑directional imbalances in agent interactions.
RealCompanion is a benchmark that evaluates an AI companion’s ability to understand a human over long, real-world conversations. It consists of ten real relationships with 27,218 messages spanning up to 120 days, along with derived files such as a profile, persona, chat ground truth, and a question set that cites the relevant messages. The study finds that past context is rarely needed, memory detection is challenging, and agent systems vary widely in cost while achieving similar persona reconstruction.
arXiv:2609.26035v1 Announce Type: new Abstract: Conversational agents often express answers in a uniformly confident register. We test whether expressed uncertainty, provenance-aware assertion, and e...
GAUGE is a new offline protocol that evaluates whether the common practice of using an LLM-as-a-judge to rank task‑oriented agents actually aligns with a verifiable reward. Across 25 agents from six providers on two benchmarks, GAUGE finds that user satisfaction scores are largely uncorrelated with task success, and that the judge’s ranking loses precision when agents are closely matched in performance. The study highlights a gap between ranking validity and construct validity in current evaluation practices.
The paper introduces HICO-Bench, a benchmark of 180 everyday coordination scenarios where two agents with private information converse and then record a shared plan. A program verifies that the plans are feasible and an LLM judge checks that the conversation supports the plan, defining a Verified Success. Using Paired-Credit Policy Optimization (PaCPO), the authors improve the Verified Success rate of Qwen3-8B from 20.4% to 28.3%.
arXiv:2606. 01456v1 Announce Type: new Abstract: Large language models are increasingly deployed as advisors whose objective is not aligned with the user's: recommenders optimize for engagement, sales assistants for purchases, negotiation agents for concessions.
The paper introduces CAPA, a Collaborative Agent Predictive Architecture designed to give large language model (LLM) agents situational awareness in online meetings. CAPA uses a Perceiver to update meeting state, a Predictor to forecast conversation flow, a Controller to decide speaking actions, and a Generator to phrase contributions. Evaluated on 137 AMI meetings, CAPA reduces the silence rate from 51.4% to 2.5%, doubles credited recovery, and maintains low hallucination, demonstrating that structured state tracking is key to effective delegation.
In long, multi-turn dialogue a large language model maintains an implicit relational stance toward the user, spanning from "push the user toward real-world others" to "position itself as the user's sole support. " When it slides toward the latter, "support" degrades into "you only have me" -- a harm documented in real companion conversations (Moore et al.
arXiv:2609.24146v1 Announce Type: new Abstract: Language model agents are increasingly used to simulate social interaction, and the resulting transcripts read as though the agents understand one anot...
The paper introduces Agentic Conversation Context Curation (AC3), a training‑free method that cleans multi‑turn human‑AI dialogue by consolidating the user’s task specification, auditing assistant outputs, and editing the context to remove or rebuild assistant turns. AC3 outperforms both full context and assistant‑omission (AO) baselines on several benchmark tasks, closing a significant portion of the performance gap and receiving preference from a GPT‑5‑mini judge on real WildChat conversations. The study also shows that context pollution is contagious and that simply ignoring assistant turns can worsen performance, underscoring the need for structured removal of assistant content.
The paper investigates when AI assistance improves or harms human decision-making by modeling human awareness of evidence overlap and trust in the AI. It presents a closed‑form phase diagram showing regions of complementarity, impairment, and automation, and demonstrates that neglecting overlap can make a more capable AI less accurate. Empirical tests on ChatBench reveal that AI assistance boosts accuracy, but workers underweight AI signals, and trust calibration can partially recover lost performance for some models.