Product-Design

TL;DR Agentic performance isn’t just about doing more; it’s about going back. In GSM-Agent—a controllable, tool-using version of GSM8K—top models only reach ~65–68% accuracy, and the strongest predictor of success is a high revisit ratio: deliberately returning to a previously explored topic with a refined query. That’s actionable for enterprise AI: design agents that can (1) recognize incomplete evidence, (2) reopen earlier lines of inquiry, and (3) instrument and reward revisits. ...

TL;DR In LLM multi‑agent systems, how a model thinks matters more than how big it is. Explicit reasoning (thinking mode / CoT) creates a Persuasion Duality: sharing a model’s reasoning makes it far better at convincing others, while enabling the model’s own reasoning mode makes it far harder to convince. This shifts best practices for agent design, governance, and product UX. Why this paper matters Cognition—not just parameter count—now drives the social dynamics of agent swarms. For Cognaptus clients building agent workers (ops, compliance, research, trading), the result is practical: toggling reasoning changes not just accuracy, but influence. Your deployment choices can tilt a network toward consensus, stalemate, or resilient truth‑seeking. ...

When multi‑turn chats stretch past a dozen turns, users lose the thread: which requests are satisfied, which are ignored, and which have drifted? OnGoal (UIST’25) proposes a pragmatic fix: treat goals as first‑class objects in the chat UI, then visualize how well each model response addresses them over time. It’s less “chat history” and more goal telemetry. What OnGoal actually builds OnGoal augments a familiar linear chat with three concrete layers of structure: ...

TL;DR When language‑model agents compete as teams and meet the same opponents repeatedly, they cooperate more—even on the very first encounter. This “super‑additive” effect reliably appears for Qwen3 and Phi‑4, and changes how we should structure agent ecosystems at work. Why this matters (for builders and buyers) Most enterprise agent stacks still optimize solo intelligence (one bot per task). But real workflows are competitive–cooperative: sales vs. sales, negotiators vs. suppliers, ops vs. delays. This paper shows that if we architect the social rules (teams + rematches) rather than just tune models, we can raise cooperative behavior and stability without extra fine‑tuning—or even bigger models. ...