LLMs

The best kind of privacy leak is the one you can measure. A recent paper by Huang et al. introduces a deceptively simple but powerful principle—the Entropy-Memorization Law—that allows us to do just that. It claims that the entropy of a text sequence is strongly correlated with how easily it’s memorized by a large language model (LLM). But don’t mistake this for just another alignment paper. This law has concrete implications for how we audit models, design prompts, and build privacy-aware systems. Here’s why it matters. ...

When Meta and other tech giants scale back content moderation, the gap isn’t just technical—it’s societal. Civil society organizations (CSOs), not corporations, are increasingly on the frontlines of monitoring online extremism. But they’re often armed with clunky tools, academic prototypes, or opaque black-box models. A new initiative—highlighted in Civil Society in the Loop—challenges this status quo by co-designing a Telegram monitoring tool that embeds human feedback directly into its LLM-assisted classification system. The twist? It invites civil society into the machine learning loop, not just the results screen. ...

The most radical idea in Michael I. Jordan’s latest manifesto isn’t a new model, a benchmark, or even a novel training scheme. It’s a reorientation. He argues that we’ve misdiagnosed the nature of intelligence—and in doing so, we’ve built AI systems that are cognitively brilliant yet socially blind. The cure? Embrace a collectivist, economic lens. This is not techno-utopianism. Jordan—a towering figure in machine learning—offers a pointed critique of both the AGI hype and the narrow symbolic legacy of classical AI. The goal shouldn’t be to build machines that imitate lone geniuses. It should be to construct intelligent collectives—systems that are social, uncertain, decentralized, and deeply intertwined with human incentives. In short: AI needs an economic imagination. ...

In the world of LLM fine-tuning, stronger usually means better. But what if we’ve been looking at supervision all wrong? A provocative new paper introduces the Delta Learning Hypothesis, arguing that LLMs can learn just as well—sometimes even better—from weak data, as long as it’s paired. The trick isn’t in the absolute quality of the training signals, but in the difference—the delta—between them. Like a coach pointing out small improvements, even bad examples can teach if they highlight how one is slightly better than another. ...

Why rent a Ferrari when a fine-tuned e-bike can get you to class faster, cheaper, and on your own terms? That’s the question quietly reshaping AI in education, as shown by Solano et al. (2025) in their paper Narrowing the Gap. The authors demonstrate that with supervised fine-tuning (SFT), smaller open-source models like Llama-3.1-8B and Qwen3-4B can rival proprietary giants like GPT-4.1 when explaining C programming error messages to students. More strikingly, they achieve this with better privacy, lower cost, and pedagogical nuance that large models often overshoot. ...

Machine unlearning, once a fringe technical curiosity, is fast becoming a legal and ethical imperative. With increasing regulatory demands like the GDPR’s “right to be forgotten,” AI developers are being asked a hard question: Can a large language model truly forget? A new paper from researchers at TUM and Mila provides an unexpectedly elegant answer. Instead of fighting model collapse—the phenomenon where iterative finetuning on synthetic data causes a model to forget—they propose embracing it. ...

When a network fails, it doesn’t whisper its problems—it screams in silence. Packet drops, congestion, and flapping links rarely announce themselves clearly. Engineers must piece together clues scattered across logs, dashboards, and telemetry. It’s a detective game where the evidence hides behind obscure port counters and real-time topological chaos. Now imagine handing this job to a Large Language Model. That’s the bold challenge taken up by researchers in “Towards a Playground to Democratize Experimentation and Benchmarking of AI Agents for Network Troubleshooting”. They don’t just propose letting LLMs debug networks—they build an entire sandbox where AI agents can learn, act, and be judged on their troubleshooting skills. It’s not theory. It’s a working proof-of-concept. ...

If you’ve looked at any leaderboard lately—from SWE-Bench to WebArena—you’ve probably seen impressive numbers. But how many of those reflect real capabilities of AI agents? This paper by Zhu et al. makes a bold claim: agentic benchmarks are often broken, and the way we evaluate AI agents is riddled with systemic flaws. Their response is refreshingly practical: a 33-point diagnostic called the Agentic Benchmark Checklist (ABC), designed not just to critique, but to fix the evaluation process. It’s a must-read not only for benchmark creators, but for any team serious about deploying or comparing AI agents in real-world tasks. ...

The financial industry has always prided itself on cold precision. For decades, quantitative models and spreadsheets dominated boardrooms and trading desks. But that orthodoxy is now under siege. Not from another statistical breakthrough, but from something surprisingly human-like: Large Language Models (LLMs). Recent research shows a dramatic shift in how AI—particularly LLMs like GPT-4 and LLaMA—is being integrated across financial workflows. Far from just summarizing news or answering earnings call questions, LLMs are now organizing entire investment pipelines, fine-tuning themselves on proprietary data, and even collaborating as autonomous financial agents. A recent survey by Mahdavi et al. (2025) categorized over 70 state-of-the-art systems into four distinct architectural frameworks, offering us a lens through which to assess the future of financial AI. ...

If the future of reasoning in large language models (LLMs) doesn’t lie in human-tweaked datasets or carefully crafted benchmarks, where might it emerge? According to SPIRAL, a recent framework introduced by Bo Liu et al., the answer is clear: in games. SPIRAL (Self-Play on zero-sum games Incentivizes Reasoning via multi-Agent muLti-turn reinforcement learning) proposes that competitive, turn-based, two-player games can become a reasoning gymnasium for LLMs. It provides an automated and scalable path for cognitive skill acquisition, sidestepping human-curated data and rigid reward functions. ...