Tag: LLMs
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Undetectable Backdoors: What Model Scanning Cannot Catch
Microsoft shipped a backdoor scanner in February. An ICML 2026 proof says a class of backdoors is undetectable. Both are right. Here is the gap.
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Governance Decay: How Compaction Deletes Agent Safety Rules
A June 2026 benchmark shows context compaction doesn’t fade an agent’s safety rules, it deletes them outright, and attackers can force the drop.
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What Americans Actually Use AI For: The Task Data
Task-level data shows US AI use: rank 12 of 121 countries, personal over work, a 13x gap between states. What the numbers say and where they stop.
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How Researchers Actually Read an LLM’s Mind
Sparse autoencoders and circuit tracing gave us wiring diagrams of LLMs. Then simple probes started winning. The honest state of interpretability in 2026.
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How AI Detectors Actually Work, and Why They Fail
AI text detectors run on perplexity, trained classifiers, and watermarks. Peer-reviewed testing shows where each mechanism breaks, and who pays.
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Is ChatGPT a Product? The Lawsuits Forcing an Answer
A Florida filing against OpenAI joins Raine and Garcia in testing whether an LLM is a product that can be defective. The definition decides who pays.
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How Many AI Lawsuits Are There? The Count Is Broken
A docket search finds 2,189 state court cases mentioning AI, 74% filed in 2026. Most are not AI disputes. Here is what broke the count.
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GGUF vs GPTQ vs AWQ: Which Quantization to Use
GGUF, GPTQ, AWQ, NF4, FP8 and MXFP4 solve different problems. How each format works, where each one wins, and which fits your deployment.
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KV Cache Explained: The Memory Math of LLM Inference
The KV cache, not parameter count, decides how many users an LLM can serve. The exact memory math, GQA and MLA compression, and caching economics.
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Why a 1M-Token Model Only Reasons Over 200K
Models advertise 1M-token windows but reason reliably over far less. The positional-encoding reason why, and how to measure your real ceiling.
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The Jailbreak Hiding in Your JSON Schema
A CCS 2026 paper hides jailbreaks in JSON schemas, hitting 94-99% success against GPT-5 and Gemini. Why prompt filters never see it.
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Ghost Vectors: Deleted Embeddings Stay Recoverable
Researchers tested three vector databases and found deleted embeddings stay intact on disk, recoverable at rates that break GDPR and HIPAA.
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How Model Merging Actually Combines Separate LLMs
Some top open-weight models are merged, not trained. The math behind task vectors, TIES, DARE, and why the technique works at all.
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How Model Quantization Actually Works: INT8 to INT4
A 70B model needs 140GB at full precision, 35GB at INT4. The rounding math, why naive quantization breaks, and how GPTQ and AWQ fix it.
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How LLM Tokenization Actually Works: BPE Explained
The algorithm behind every LLM tokenizer was built in 1994 to compress files, not language. The mechanism, and why it breaks on math and non-English text.
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How Mixture-of-Experts Actually Routes Every Token
DeepSeek V4 holds 1.6 trillion parameters and uses a fraction per token. The routing math, why naive versions collapse, and the modern fix.
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Same Model, 20-Point Gap: Why Coding Benchmarks Mislead
Claude Opus 4.6 scores 58% or 80% on the same benchmark depending only on which harness wraps it. Here is why coding agent scores mislead.
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How an Export Law Built for Chips Took Down Fable 5
A private Commerce Department letter used a dormant 2018 export authority to shut down Fable 5 and Mythos 5 worldwide. Here is the mechanism.
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MCP Goes Stateless on July 28. Its Poisoning Problem Stays
MCP’s July 28 spec removes sessions and adds response caching. That solves scaling headaches and quietly widens the window for tool poisoning.
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ShareLock Splits Malicious Prompts Across AI Agent Tools
Researchers used Shamir’s secret sharing to hide prompt-injection payloads across MCP tools, beating detectors with a 90%+ success rate.
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