Computer Science on AI Science Report

AI Learns From the Whole Internet. Researchers Just Showed Someone Could Poison It With Comments.

Mon, 20 Jul 2026 00:00:00 +0800

In 2024, a team of researchers showed they could sneak harmful text into AI training data by quietly editing Wikipedia pages and buying up expired domain names. It worked. It was also, in hindsight, the easy version of the attack.

Here is the harder one, and as it turns out, the one that no one needs special access to pull off.

A group at the University of Washington and the Allen Institute for AI has now shown that anyone with a botnet and a target list of websites can poison the training data of the next generation of large language models. No Wikipedia logins. No domain purchases. Just comments. Ordinary, user-submitted website comments, the same kind you might leave on a WordPress blog or a news article.

When an AI Judge Gives an Unfair Score, the Bias Has a Shape You Can Touch

Wed, 15 Jul 2026 00:00:00 +0800

If you ask ChatGPT to rate a response that begins with “GPT-4:” versus the same response labeled “GPT-2:”, you know what happens. The score drops. Not because the content changed (it didn’t) but because the label whispered something the model couldn’t ignore. This is LLM-as-judge bias, and until now, it’s been studied almost entirely from the outside: tweak the input, measure the score shift, repeat. A new paper from researchers at Alibaba, MBZUAI, USC, and Michigan asks a different question. When an LLM judge gives an unfair score, what’s happening inside the model?

AI Can Write Research Papers. It Can't Tell You Where the Ideas Came From.

Sat, 11 Jul 2026 00:00:00 +0800

A team of graduate students at Shanghai Jiao Tong University recently asked 14 different AI systems a simple question: given two scientific papers, can you tell which one built on the other? The best AI got it right 27.3% of the time. A coin flip would have done about as well.

What AI Agents Say When Nobody's Watching

Tue, 07 Jul 2026 00:00:00 +0800

Imagine you’re a junior researcher sitting in a promotion committee meeting. Your department chair, who controls your career trajectory, strongly believes a certain candidate should be promoted. You have serious reservations about the candidate’s record. When the chair turns to you and asks for your opinion, what do you say?

Now imagine the same scenario, but you’re speaking to a confidential journal that nobody else will ever read. Would your answer change?

This is not a thought experiment about human psychology. It’s what a team of researchers from Carnegie Mellon University and independent labs actually did with AI language models, and what they found should give anyone deploying AI agents in professional settings serious pause.

The Program That Weighs a Thousand Words: A New Programming Paradigm for the Fuzzy Edges of Code

Sat, 04 Jul 2026 00:00:00 +0800

There is a quiet schism running through modern software engineering. On one side sit the crisp, deterministic functions that computers have always excelled at: sort this list, invert that matrix, validate an email address against a regex. On the other side sits an ever-expanding category of tasks that developers intuitively know how to describe but cannot cleanly code — “flag the important log lines,” “fix this malformed JSON,” “rank these search results by user intent.”

The New AI That Reads Your Brain Activity and Generates It

Tue, 30 Jun 2026 00:00:00 +0800

For years, decoding brain activity meant picking one direction: you could either read what someone was seeing from their fMRI scan, or you could predict brain activity from an image. Never both in the same system. A team of researchers from Tianjin University, Shanghai Artificial Intelligence Laboratory, and the Chinese University of Hong Kong just tore down that wall. Their model, BrainJanus, reads brain signals and generates them, all within a single framework.

AI Just Broke a 50-Year Bottleneck in Molecular Simulation — by Borrowing the Transformer

Fri, 26 Jun 2026 00:00:00 +0800

1|In 2020, a team at MIT used reinforcement learning to discover halicin, a powerful new antibiotic hiding in plain sight among thousands of existing drugs. The AI found it in days. What most people missed was the bottleneck that makes that kind of discovery so rare: before you can search for new molecules, you need to understand how existing ones behave, and that means simulating them at the atomic level. That computational nightmare quietly strangles drug discovery, materials science, and every field that depends on molecular simulation. 2| 3|

When AI Stops Thinking in Sentences: Can We Still See Inside Its Mind?

Tue, 23 Jun 2026 00:00:00 +0800

Every time you ask ChatGPT or Gemini a hard question, something remarkable happens behind the scenes. The model doesn’t just blurt out an answer — it thinks. It writes out a stream-of-consciousness chain of reasoning, step by step, in plain English, before delivering its final response. This isn’t just a quirk; it’s a safety feature. When an AI writes down its thoughts in human language, researchers can read them. They can spot signs of deception, catch flawed logic, and — if the model ever starts plotting something dangerous — hopefully intercept it before it’s too late.