Autonomous Agents

Opening — Why this matters now Emotional support from AI has quietly moved from novelty to expectation. People vent to chatbots after work, during grief, and in moments of burnout—not to solve equations, but to feel understood. Yet something subtle keeps breaking trust. The responses sound caring, but they are often wrong in small, revealing ways: the time is off, the location is imagined, the suggestion doesn’t fit reality. Empathy without grounding turns into polite hallucination. ...

Opening — Why this matters now Temporal reasoning has always been the Achilles’ heel of symbolic AI. The moment time becomes quantitative—minutes, deadlines, durations—logic programs tend to balloon, grounders panic, and scalability quietly exits the room. This paper lands squarely in that discomfort zone and does something refreshingly unglamorous: it makes time boring again. And boring, in this case, is good for business. ...

Opening — Why this matters now The AI industry has grown comfortable with a flattering assumption: if a model can reason, it can plan. Multi-step logic, chain-of-thought traces, and ever-longer context windows have encouraged the belief that we are edging toward systems capable of sustained, goal-directed action. SokoBench quietly dismantles that assumption. By stripping planning down to its bare minimum, the paper reveals an uncomfortable truth: today’s large reasoning models fail not because problems are complex—but because they are long. ...

Opening — Why this matters now Large language models are supposed to speak our language. Yet as they become more capable, something uncomfortable emerges: when pushed to cooperate efficiently, models often abandon natural language altogether. This paper shows that modern vision–language models (VLMs) can spontaneously invent task-specific communication protocols—compressed, opaque, and sometimes deliberately unreadable to outsiders—without any fine-tuning. Just prompts. ...

Opening — Why this matters now LLM agents are no longer toys. They book flights, write emails, control vehicles, and increasingly operate in environments where getting it mostly right is not good enough. In real-world deployments, the failure mode that matters most is not ignorance—it is false confidence. Agents act when they should hesitate, fabricate when they should refuse, and choose when they should ask. ...

Opening — Why this matters now Clinical AI has quietly hit a ceiling. Over the past five years, large language models trained on electronic health records (EHRs) have delivered impressive gains: better coding, stronger risk prediction, and even near‑physician exam performance. But beneath those wins lies an uncomfortable truth. Most clinical foundation models still treat patients as documents—static records to be summarized—rather than systems evolving over time. ...

Opening — Why this matters now Agentic AI is having a credibility problem. Not because agents can’t browse, code, or call tools—but because we still train them like they’re taking a final exam with no partial credit. Most agentic reinforcement learning (RL) systems reward outcomes, not process. Either the agent finishes the task correctly, or it doesn’t. For short problems, that’s tolerable. For long-horizon, tool-heavy reasoning tasks, it’s catastrophic. A single late-stage mistake erases an otherwise competent trajectory. ...

Opening — Why this matters now Inference-time scaling has quietly replaced parameter scaling as the most interesting battleground in large language models. With trillion-parameter training runs yielding diminishing marginal returns, the industry has pivoted toward how models think together, not just how big they are. Mixture-of-Agents (MoA) frameworks emerged as a pragmatic answer: run multiple models, stack their outputs, and hope collective intelligence beats individual brilliance. It worked—up to a point. But most MoA systems still behave like badly moderated panel discussions: everyone speaks, nobody listens. ...

Opening — Why this matters now Audio-first interfaces are everywhere. Voice assistants, call-center bots, in-car copilots, and accessibility tools all rely on large audio-language models (LALMs) that promise to hear and think at the same time. Yet in practice, something awkward happens: the same model that reasons fluently when reading text suddenly becomes hesitant, shallow, or just wrong when listening to speech. ...

Opening — Why this matters now Modern deep learning quietly assumes a comforting fiction: that training is memoryless. Given the current parameters (and maybe the optimizer buffers), tomorrow’s update shouldn’t care about yesterday’s data order, augmentation choice, or micro-step path. This assumption underwrites theory, stabilizes intuition, and keeps whiteboards clean. Reality, however, has been less cooperative. Practitioners know that order matters, momentum carries ghosts of past gradients, and small curriculum tweaks can echo far longer than expected. Yet until now, there has been no clean, operational way to measure whether training truly forgets—or merely pretends to. ...