United States Virgin Islands: A white field with a yellow eagle between a blue V and I, holding a laurel sprig in one talon and three arrows in the other.
United States: Thirteen red and white stripes with fifty white stars on a blue canton.United Kingdom: The Union Flag: a red cross edged in white over red and white diagonals on blue.Ghana: Red, gold and green bands with a black five-pointed star centred on the gold.
Sri Lanka: A gold lion holding a sword on dark red, with a bo leaf at each corner and green and orange stripes alongside.The ThunderCats emblem: a cat's head in profile, cut out of a red disc.
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interesting white papers

  • First page of Craig Gentry, "Fully Homomorphic Encryption Using Ideal Lattices", STOC 2009.
    Fully Homomorphic Encryption Using Ideal Lattices — Gentry, 2009
  • First page of Craig Gentry and Yongwoo Lee, "Fully Homomorphic Encryption for Matrix Arithmetic".
    Fully Homomorphic Encryption for Matrix Arithmetic — Gentry and Lee, 2025
  • First page of Sun, Li and Zhang, "zkLLM: Zero Knowledge Proofs for Large Language Models", CCS 2024.
    zkLLM: Zero Knowledge Proofs for Large Language Models — Sun, Li and Zhang, 2024
  • First page of Silver and others, "Mastering the game of Go with deep neural networks and tree search", Nature, January 2016.
    Mastering the game of Go with deep neural networks and tree search — Silver and others, 2016
  • First page of Rajput and Madisetti, "A Pruning Framework for Bias Mitigation in Large Language Models".
    A Pruning Framework for Bias Mitigation in Large Language Models — Rajput and Madisetti, 2026
  • First page of the Kimi K3 technical report, "Kimi K3: Open Frontier Intelligence", with the main results chart.
    Kimi K3: Open Frontier Intelligence — Kimi Team, 2026
  • First page of Shaik and others, "Practical Attacks Against Privacy and Availability in 4G/LTE Mobile Communication Systems", NDSS 2016.
    Practical Attacks Against Privacy and Availability in 4G/LTE — Shaik and others, 2016
  • First page of Garfinkel and others, "Compatibility is Not Transparency: VMM Detection Myths and Realities", HotOS XI 2007.
    Compatibility is Not Transparency — Garfinkel and others, 2007
  • First page of Boming Ning and Kiseop Lee, "Advanced Statistical Arbitrage with Reinforcement Learning".
    Advanced Statistical Arbitrage with Reinforcement Learning — Ning and Lee, 2024

Fully Homomorphic Encryption Using Ideal Lattices

Craig Gentry · STOC '09, Bethesda, Maryland, May 2009

The one that turned a thirty-year open question into an engineering problem. The move is bootstrapping, and it is the kind of idea you can hold in your head afterwards: if a scheme can evaluate its own decryption circuit, it can refresh its own ciphertexts, and a scheme that can refresh can compute forever. Everything since is an argument about the constant factors.

Fully Homomorphic Encryption for Matrix Arithmetic

Craig Gentry and Yongwoo Lee · Preprint, October 2025

Sixteen years on from the paper above, the same author on the constant factors. Encrypted matrix multiplication comes down to four multiplications of ciphertext coefficients, with none of the slot-to-coefficient conversion and ring switching that made the operation expensive — which matters because matrix arithmetic is what machine learning is, and encrypted inference is bounded by exactly this.

zkLLM: Zero Knowledge Proofs for Large Language Models

Haochen Sun, Jason Li and Hongyang Zhang · CCS '24, Salt Lake City, October 2024; arXiv:2404.16109v1

A proof that a given output came from a given model, without the weights ever leaving the prover. The interesting half is not the cryptography but what it is aimed at: the parameters are the intellectual property and the output is the thing being regulated, and those two facts usually point in opposite directions. This is a construction where they do not. The hard part is attention, and tlookup and zkAttn are where the paper earns its keep.

Mastering the game of Go with deep neural networks and tree search

David Silver, Aja Huang, Chris J. Maddison and seventeen others · Nature 529, 484–489, 28 January 2016; doi:10.1038/nature16961

AlphaGo, and the sentence in the abstract that dates the whole field: a feat previously thought to be at least a decade away. The architecture is two networks and a search — a policy network to narrow the breadth, a value network to truncate the depth, Monte Carlo tree search to spend the compute where it pays — and the training pipeline runs supervised learning on human games into self-play reinforcement learning, which is the move everything since has copied. Worth reading now for how modest it sounds while doing it.

A Pruning Framework for Bias Mitigation in Large Language Models

Amrita Singh Rajput and Vijay K. Madisetti · Accepted, IEEE Open Journal of the Computer Society; doi:10.1109/OJCS.2026.3701986

Pruning is a compression technique here turned into a scalpel: rank whole transformer blocks by a score that mixes utility, redundancy and bias attribution, then cut the ones that are all three at once. The claim worth checking is the localisation one — that bias sits in identifiable blocks rather than smeared across the network — and the numbers they report for it are honest about the trade, 0.467 absolute TVD off Qwen2.5-0.5B at no perplexity cost, 0.250 off TinyLlama-1.1B at twelve percent.

Kimi K3: Open Frontier Intelligence

Kimi Team · Technical report, August 2026; arXiv:2607.24653v2

Forty-seven pages of a frontier lab showing its working, which is the rare part — a 2.8T mixture of experts with 104B active, sixteen of 896 routed experts per token, a million tokens of context, and the weights released. The half worth the afternoon is not the benchmark table on page one but the infrastructure chapters behind it: the algorithm-and-system co-design for Kimi Delta Attention, expert-parallel training balanced by construction, and agentic RL held together by persistent rollout and sandbox state. It is also candid about trailing the closed models it lists itself against.

Practical Attacks Against Privacy and Availability in 4G/LTE Mobile Communication Systems

Altaf Shaik, Ravishankar Borgaonkar, N. Asokan, Valtteri Niemi and Jean-Pierre Seifert · NDSS '16, San Diego, February 2016; arXiv:1510.07563v3

The paper under the IMSI-catcher detectors people carry now, EFF's Rayhunter among them. It is a careful read of the LTE access network specification followed by a USRP and a bill of materials, and the finding is that a protocol designed to fix 2G's missing mutual authentication still leaks location to a passive listener and can be made to drop a phone back to 2G on command. Downgrade is the attack that never stops working.

Compatibility is Not Transparency: VMM Detection Myths and Realities

Tal Garfinkel, Keith Adams, Andrew Warfield and Jason Franklin · HotOS XI, 2007

Six pages that end an argument. A hypervisor can run the guest's software correctly and still be nothing like the hardware it claims to be, because running it correctly is a statement about semantics and detection is a question about timing, cache, errata and every other place the abstraction is not the machine. The conclusion — that a transparent VMM is infeasible, not merely unbuilt — is the sort a paper is braver for stating plainly.

Advanced Statistical Arbitrage with Reinforcement Learning

Boming Ning and Kiseop Lee · Purdue University, March 2024; arXiv:2403.12180v1

Pairs trading without the Ornstein–Uhlenbeck assumption. Instead of fitting a spread to a process the market has not agreed to follow, they define an empirical mean reversion time and search for the basket that minimises it, then hand the entry and exit to a policy whose state is the recent shape of the move rather than the distance from a long-run mean. Worth reading for the reframing even if you never trade it.