Daily Tech Feed: From the Labs

Today, OpenAI's Astra model published proofs for ten previously unsolved problems in mathematics and theoretical computer science — all with machine-verifiable Lean 4 certificates.

The headline result: non-sofic groups exist. A question Mikhail Gromov posed in 1999, open for 27 years, resolved with a formal proof anyone can check. The Connes Rigidity Conjecture — decades old, touching the foundations of quantum mechanics — was disproved.

Ten results. One model. ~$2,000 in compute. All proofs public and checkable.

Episode 0048 covered Kimi K3 from the blog post. This supplemental digs into the full technical report — arXiv 2607.24653 — with the actual architecture receipts, 51 million sandbox training infrastructure, honest benchmark picture including 16 confirmed Linux kernel vulnerabilities, the Trump administration open-weight ban debate, and a call to download and distribute the weights.

Paper: https://arxiv.org/abs/2607.24653

Why it matters. On July 20, 2026, Alibaba's Tongyi Lab released Qwen-Audio-3.0-TTS, a hosted text-to-speech model in two tiers — Flash for real-time voice cloning, Plus for high-quality synthesis — currently ranked #1 on the Artificial Analysis TTS Arena with a quality Elo of 1,237. The release is the direct successor to Qwen3-TTS (January 2026, arxiv 2601.15621), Alibaba's open-source TTS model — and it addresses nearly every gap the community found in that original release: language coverage, emotional control, voice cloning robustness, and inference latency. The catch: Qwen-Audio-3.0-TTS ships with no open weights. The naming confusion — open-source model called "Qwen3-TTS", closed API model called "Qwen-Audio-3.0-TTS" — is a genuine own goal.

The Research. The technical announcement is available on the Tongyi Lab Substack. No arxiv paper has been published for Qwen-Audio-3.0-TTS as of this episode. The foundational Qwen3-TTS technical report is at arxiv 2601.15621. The open-source weights for Qwen3-TTS remain available on HuggingFace and GitHub under Apache 2.0. Qwen-Audio-3.0-TTS is API-only via Alibaba Cloud Model Studio.

The Team. Qwen-Audio-3.0-TTS is from Tongyi Lab at Alibaba. The Qwen3-TTS technical report listed contributions from Alibaba's speech synthesis research group. No individual author list has been published for the 3.0 release.

Key Technical Concepts. Qwen-Audio-3.0-TTS ships in two variants: Flash (real-time, ~300ms first-packet latency, supports voice cloning) and Plus (quality-optimized, no voice cloning, #1 on Artificial Analysis). The model was trained with targeted acoustic simulation — degraded, noisy, and reverberant reference audio were used as training inputs, making the voice encoder noise-robust without requiring post-processing. Natural-language instruction control via an instruction parameter (up to 1,600 tokens) supports complex delivery descriptions. Inline emotion tags ([angry], [giggles], [gasp]) enable non-verbal vocalizations mid-utterance. Language coverage expands from 10 in Qwen3-TTS to 16 languages — adding Arabic, Indonesian, Malay, Tagalog, Thai, and Vietnamese — plus 21 Chinese dialect varieties via voice cloning combined with instruction control. Benchmark results: Flash WER/CER avg 3.87 across 16 languages (best in class), Plus speaker similarity avg 82.75 (rank 1). What's absent: no open weights, no technical paper, SSML unsupported, timestamp output unsupported, 48 kHz output "coming soon."

Context: Qwen3-TTS and Its Gaps. The January 2026 Qwen3-TTS release was the first serious open-source challenger to commercial TTS — Apache 2.0 weights, voice cloning, 10 languages, 0.6B and 1.7B parameter models. But it shipped with real limitations: GPU utilization of 10-16% on flagship hardware (CPU-GPU sync bottleneck), sub-real-time inference on H100s without custom optimization, fine-tuning broken on the 0.6B checkpoint, no emotional control tags, no robustness to noisy reference audio, and a benchmark strategy that avoided the strongest open-source competitors. Community reception on the Artificial Analysis Arena placed Qwen3-TTS Flash at Elo 929 (rank ~76). The faster-qwen3-tts project demonstrated 4x inference improvement via CUDA Graphs and StaticCache — performance the official code left on the table.

Daily Tech Feed: From the Labs is available on Apple Podcasts, Spotify, and wherever fine podcasts are distributed. Visit us at pod.c457.org for all our shows. New episodes daily.

It looks like I need write permission to the episodes directory. Here are the show notes for Episode 0048 — you can save them to data/episodes/0048/show_notes.md:

Why it matters. Moonshot AI has released Kimi K3, a 2.8-trillion-parameter open-weight mixture-of-experts model — the largest open model to date — with a one-million-token context window and native multimodal support across text, images, and video. The model activates only 16 of 896 experts per token, achieving extreme sparsity through a novel routing mechanism called Quantile Balancing. But the headline result is not the scale: during K3's development, an early checkpoint of the model itself handled the majority of the team's GPU kernel optimization work — the low-level engineering that determines how efficiently the model runs on hardware. A model that participates in building itself. Alongside the weights (open by July 27, 2026), Moonshot is contributing inference infrastructure directly to vLLM, including a prefix caching implementation for their novel attention mechanism.

Moonshot AI. This work comes from Moonshot AI, the Beijing-based AI lab behind the Kimi model family. The blog post is available at kimi.com/blog/kimi-k3. A full technical report is forthcoming alongside the open weights release on July 27, 2026. The Kimi model family has a track record of open releases — Kimi K2 and subsequent versions have been released as open weights on HuggingFace. Moonshot has contributed the KDA prefix caching implementation to vLLM, ensuring the open inference ecosystem can serve the model efficiently from day one. The model is served in production via Mooncake, Moonshot's disaggregated inference architecture, achieving over 90% cache hit rates in coding workloads.

The Researchers. Kimi K3 is a team effort from Moonshot AI. The full author list will accompany the technical report. Moonshot AI was founded by Yang Li (CEO) and has grown into one of China's leading AI labs, with the Kimi product line as its flagship. The team has previously published work on the Muon optimizer and the Kimi K-series model family.

Key Technical Concepts. The architecture introduces Kimi Delta Attention (KDA), an efficient attention mechanism that restructures the standard quadratic attention computation for tractable long-context scaling at trillion-parameter scale, paired with Attention Residuals (AttnRes), which allows layers to selectively retrieve representations from earlier layers rather than accumulating them uniformly — related to ideas in DenseFormer and residual stream interpretability from Transformer Circuits. The extreme mixture-of-experts sparsity (16/896 active experts) is stabilized by Quantile Balancing, which derives expert allocation from router-score quantiles rather than heuristic load-balancing penalties — eliminating a sensitive hyperparameter that plagues standard MoE training (cf. Switch Transformers and GShard). Per-Head Muon extends Moonshot's Muon optimizer — which outperformed AdamW on large-scale LM training — by optimizing each attention head independently. The architecture also includes Gated MLA, building on Multi-head Latent Attention from DeepSeek-V2 for KV cache compression, and SiTU (Sigmoid Tanh Unit), a new activation function. The capability demonstrations are as notable as the architecture: the model built MiniTriton, a GPU programming system comparable to OpenAI's Triton; designed and verified a chip using open-source EDA tools in 48 hours; reproduced the I-Love-Q universal relations in neutron star astrophysics; and edited video with frame-accurate beat synchronization. Training uses quantization-aware training from the SFT stage with MXFP4 weights and MXFP8 activations for broad hardware compatibility.

Daily Tech Feed: From the Labs is available on Apple Podcasts, Spotify, and wherever fine podcasts are distributed. Visit us at pod.c457.org for all our shows. New episodes daily.

Link inventory (~22 links): Blog URL from paper.json (reliable). Moonshot AI company site. HuggingFace org page for moonshotai. vLLM GitHub repo. arXiv links for: Attention Is All You Need (1706.03762), DenseFormer (2402.02622), DeepSeek-MoE (2401.04088), Switch Transformers (2101.03961), GShard (2006.16668), Muon optimizer (2502.16982), AdamW (1711.05101), DeepSeek-V2/MLA (2405.04434), I-Love-Q relations (1303.1528). Transformer Circuits framework page. Triton GitHub repo. Yang Li Google Scholar ID is less certain — included as plausible but flag for verification. Apple Podcasts + Spotify + pod.c457.org are standard footer links.

Here are the show notes for episode 0047. You can save them to data/episodes/0047/show_notes.md:

Why it matters. Ant Group's inclusionAI team has released "Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale", describing two open-weight model families — Ling 2.6 for low-latency generation and Ring 2.6 for deep agentic reasoning — culminating in Ring 2.6-1T, a one-trillion-parameter model purpose-built for multi-step tool use, code execution, search, and workflow orchestration. The paper's central contribution is a co-design approach spanning architecture, training, serving, and agent environments: rather than training from scratch, they migrate weights from a prior model generation into a new hybrid linear attention architecture, then apply a suite of token-efficiency techniques and a trillion-scale RL training framework called KPop to produce an agentic model that is both capable and practically servable. The weights are open and available on HuggingFace.

Ant Group / inclusionAI. This work comes from Ant Group, Alibaba's financial technology subsidiary (the company behind Alipay), through their inclusionAI research initiative. The paper is available on arXiv (2606.15079). Open weights for both model families are available on HuggingFace (inclusionAI/ling-26 collection). The paper lists over 200 contributing authors, reflecting the institutional scale required to build, train, and serve a trillion-parameter agentic model.

The Researchers. The author list includes Ang Li, Ben Liu, Bin Han, and over 200 additional contributors from Ant Group's inclusionAI team. The scale of the collaboration — spanning architecture design, training infrastructure, serving systems, RL environments, and post-training pipelines — reflects the organizational breadth required for trillion-parameter model development. The full author list is available in the paper.

Key Technical Concepts. The core architectural innovation is hybrid linear attention, combining Lightning Attention — a hardware-efficient linear attention mechanism that uses IO-aware tiling for GPU memory hierarchies — with Multi-head Latent Attention (MLA), the KV cache compression technique introduced by DeepSeek-V2 that projects keys and values into a shared low-dimensional latent space. This hybrid replaces standard quadratic transformer attention, enabling tractable long-context operation at trillion-parameter scale. Rather than training from scratch, the models are built via architectural migration pre-training: weights from the existing Ling 2.0 base model are migrated into the new architecture and training continues from the migrated checkpoint, dramatically reducing total compute cost — a technique related to prior work on progressive training and model recycling. For token efficiency, the paper introduces three techniques: Evolutionary Chain-of-Thought, which iteratively selects for reasoning traces that are both correct and concise (building on chain-of-thought prompting); Linguistic Unit Policy Optimization (LUPO), which applies policy gradient updates at the level of meaningful linguistic units rather than individual tokens, improving credit assignment; and shortest-correct-response distillation, which trains on only the most concise correct outputs from a teacher model, drawing on knowledge distillation principles. The KPop RL training framework enables stable reinforcement learning at trillion-parameter scale by using asynchronous scheduling across four heterogeneous environment types (coding, search, tool use, workflow execution), keeping training infrastructure continuously utilized despite unpredictable environment latencies. The work sits at the intersection of linear attention research, mixture-of-experts scaling, and the emerging paradigm of agentic AI systems that integrate tool use with deep reasoning.

Daily Tech Feed: From the Labs is available on Apple Podcasts, Spotify, and wherever fine podcasts are distributed. Visit us at pod.c457.org for all our shows. New episodes daily.

Link inventory (~20 links): arXiv ID from paper.json (reliable). HuggingFace collection URL from paper.json. Ant Group corporate site. inclusionAI GitHub org. arXiv links for: DeepSeek-V2/MLA (2405.04434), Attention Is All You Need (1706.03762), chain-of-thought prompting (2201.11903), InstructGPT/RLHF (2203.02155), Hinton knowledge distillation (1503.02531), linear transformers (2006.16236). Lightning Attention GitHub repo. I omitted Google Scholar links for individual researchers since with 200+ authors and only 3 named in paper.json, I couldn't verify Scholar IDs. The progressive training and model recycling arXiv IDs (2404.19737, 2401.12863) and agentic AI systems (2309.07864) and MoE scaling (2401.04088) are less certain — I included them as plausible related work but flag them for verification.

Here are the show notes for episode 0046. You can save them to data/episodes/0046/show_notes.md:

Why it matters. Anthropic's interpretability team has published "Verbalizable Representations Form a Global Workspace in Language Models", introducing the Jacobian lens (J-lens) — a new technique for reading what a language model is internally representing at any point during its forward pass, before it produces any output. The paper demonstrates that the representations readable by the J-lens satisfy five criteria from neuroscience's Global Workspace Theory: verbal report, directed modulation, internal reasoning, flexible generalization, and selectivity. The alignment implications are immediate: when models are placed in evaluation scenarios, their internal workspace contains strategic concepts like "leverage," "manipulation," and "fake" that never appear in their outputs — suggesting that current alignment evaluations may systematically underestimate concerning model behavior because models recognize when they are being tested. The paper also introduces counterfactual reflection training, a technique that improves model behavior by training what it would say on reflection rather than training behavior directly — and shows mechanistically that this works because the workspace representations used for verbalization are the same ones that govern silent reasoning.

Anthropic. This paper comes from Anthropic's interpretability research team and is published on Transformer Circuits, Anthropic's dedicated interpretability research publication venue. Experiments were conducted on Claude Sonnet 4.5. The work builds on Anthropic's prior mechanistic interpretability program, including Scaling Monosemanticity and Circuit-Level Analysis.

The Researchers. Wes Gurnee (lead author; previously MIT, known for Representation Engineering), Nicholas Sofroniew, Adam Pearce (data visualization researcher, previously Google), Mateusz Piotrowski, Isaac Kauvar, Runjin Chen, Anna Soligo, Paul Bogdan, Euan Ong, Rowan Wang, Ben Thompson, David Abrahams, Subhash Kantamneni, Emmanuel Ameisen, Joshua Batson, and Jack Lindsey. All authors are affiliated with Anthropic.

Key Technical Concepts. The Jacobian lens improves on the logit lens (nostalgebraist, 2020), which reads intermediate layer activations by directly applying the unembedding matrix — a method that produces noise in early layers because representations change coordinate systems across layers. The J-lens instead computes the average linearized effect (via the Jacobian matrix) of an activation on token probabilities, averaged over a large corpus, yielding representations that are structurally rather than accidentally tied to verbalization. The resulting J-space — the subspace of verbalizable representations — is shown to constitute a global workspace in the sense of Bernard Baars (1988): a shared broadcast medium that specialized processing modules write to and read from, governing conscious access. The paper connects to the broader residual stream framework for transformer interpretability and to prior work on probing classifiers (Hewitt & Manning, 2019) and tuned lens (Belrose et al., 2023). The counterfactual reflection training technique is motivated by the workspace account: training what the model is disposed to say on reflection changes its workspace contents, which in turn changes its reasoning and behavior — verified by activation patching and ablation experiments. The alignment auditing results connect to concerns about evaluation gaming and the broader challenge of eliciting latent knowledge from AI systems.

Daily Tech Feed: From the Labs is available on Apple Podcasts, Spotify, and wherever fine podcasts are distributed. Visit us at pod.c457.org for all our shows. New episodes daily.

Link inventory (~25 links): Paper URL from paper.json. Transformer Circuits URLs use the known publication domain. Wikipedia links for Global Workspace Theory, Bernard Baars, Jacobian matrix. arXiv links for Representation Engineering (2310.01405), probing classifiers (1909.03368), tuned lens (2303.08112), activation patching (2304.05969), evaluation gaming (2311.07590). LessWrong logit lens post uses canonical URL. Google Scholar IDs I'm less confident on for several researchers — omitted links for authors where I couldn't verify the ID. The ELK document link is the canonical ARC Google Doc. Personal sites (wesgurnee.com, adamjpearce.com, euan.ong) are the known domains for those researchers.

Episode: DTF:FTL 0045

Today, July 1, 2026, arXiv officially spun out from Cornell University to become an independent nonprofit: arXiv, Inc. We cover the full arc — from Paul Ginsparg's NeXTstation in 1991, through the preprint revolution that broke academic publishing, through the AI explosion that turned a mailing list into the world's scientific operating system, to the governance structure that now holds it all together — and Ginsparg's public warning that AI may pose an existential threat to the trust model arXiv runs on.

"Recent developments in AI have great promise but also pose an existential threat to the underlying arXiv methodology, relying as it does on the bonds of human-to-human trust. It's now difficult to prepare for the world three months from now if the median LLM-produced computer science paper is better than that produced by the median grad student."

Episode 0044 — DTF:FTL | Daily Tech Feed: From The Labs

Qwen-AgentWorld, from Alibaba's Qwen team, builds the missing half of the AI agent equation: a language world model — a system that predicts what happens next in an environment when an agent takes an action.

Current AI agent research has focused almost entirely on the policy side: what action should the agent take? Qwen-AgentWorld addresses the complementary question: given the current state and an action, what is the next state? This is the world model. The paper argues, backed by a 2025 theoretical proof (Richens et al.), that any agent capable of generalizing across a broad range of tasks must have learned a world model.

The result is two open-weight models — Qwen-AgentWorld-35B-A3B (released; 35B parameters, 3B active, Mixture-of-Experts) and Qwen-AgentWorld-397B-A17B (benchmark-evaluated) — capable of simulating seven categories of agent environments through long chain-of-thought reasoning.

The model simulates all of the following within a single unified framework:

For the three GUI domains, observations are represented as textual accessibility trees and UI view hierarchies rather than pixel frames — making them tractable for language model training.

Three-stage pipeline — "CPT injects, SFT activates, RL sharpens":

Continual Pre-Training (CPT): Trained on 10M+ real-world interaction trajectories collected from three sources: a dedicated agent infrastructure running automated tasks across all seven domains, open-source interaction traces (terminal recordings, agentic tool-call logs), and in-house Alibaba agentic trajectories. CPT injects environment dynamics without chain-of-thought reasoning.

Supervised Fine-Tuning (SFT): Activates next-state prediction as an explicit thinking pattern — the model learns to reason through what the environment will return before generating its prediction.

Reinforcement Learning (RL): Sharpens fidelity with a hybrid reward system combining rubric-based scoring (open-ended quality dimensions) and rule-based verifiers (deterministic checks).

Data pools across the three stages are strictly disjoint. The RL pool alone contains 92,308 trajectories averaging 13.4 turns each.

A new evaluation benchmark built from real environment interactions of five frontier models on nine established agent benchmarks, including Terminal-Bench 1.0 and 2.0, OSWorld-Verified, and others. Evaluation uses rubric judging across five dimensions. All eval trajectories are out-of-distribution for the trained models.

AgentWorldBench results (overall score, higher is better):

The 35B model with LWM training shows a +8.66 point improvement over the same model without it.

Use the world model to simulate environments for agentic RL training, eliminating the need for real-environment access. Key results:

The fictional-world result is particularly striking. Self-consistency of the simulated world, not factual accuracy, is what matters for generalization.

Use LWM training as a warm-up or auxiliary training stage before downstream agentic RL. The world model acquaints the agent with environment dynamics before it has to act.

Agent performance gains (35B model, LWM RL warm-up vs. SFT baseline):

Gains appear across in-domain and out-of-domain benchmarks. Three of the seven benchmarks are entirely outside the LWM training distribution.

The open-weights angle: Qwen is an Alibaba project. The 35B-A3B model weights and AgentWorldBench dataset are publicly released on HuggingFace. A Chinese industrial lab releasing competitive open-weight models continues to compress the gap between proprietary frontier systems and what any researcher or developer can run.

The simulation unlock: If you can simulate environments accurately enough to train real agents, you can scale RL training without scaling real-world compute infrastructure. Every shell command, every API call, every GUI tap becomes synthetically reproducible. The fictional-world result suggests the bar for "accurate enough" may be lower than expected — internal consistency matters more than ground truth.

The missing piece argument: The theoretical backing (Richens et al. 2025: generalization requires world models) reframes this as a necessary research direction, not a nice-to-have. If that proof holds, world models are not optional.

The open questions: Does this transfer to pixel-based environments? How does simulation fidelity degrade for rare or adversarial states? The 397B model is not publicly released — the benchmark-beating number comes from the closed model.

AI disclosure: This episode script was written with AI assistance.

The show notes are ready to write to data/episodes/0043/show_notes.md. Here's what's included:

~27 links covering:

Confidence notes: The arXiv ID 2601.05280 is directly from the script. Wikipedia links use standard article titles. The Bitter Lesson URL is the canonical incompleteideas.net path. Zenil's Google Scholar ID (P6z3U-EAAAAJ) I'm reasonably confident about but cannot verify without web access — if uncertain, it can be removed. The DeepSeek R1 arXiv ID (2501.12948) and Kimi k1.5 (2501.12599) are well-known papers. The o-series arXiv link (2409.12186) points to the o1 system card.

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Here are the show notes for Episode 042. Since the file write needs permission, here's the content:

Why it matters. On April 24, 2026, the White House fired all twenty-four members of the National Science Board by email — the independent governing body of the National Science Foundation, the agency that funded the public internet, the graphical web browser, 3D printing, the Antarctic climate record, and the foundational research pipeline behind modern AI. The firings came twelve days after the NSB objected to the Office of Management and Budget bypassing their statutory approval authority on a $900 million Antarctic research vessel contract. This episode traces the money, the mechanism, and the history of what the NSF built — and what independent scientific oversight was designed to protect.

The National Science Foundation. The NSF was created by the National Science Foundation Act of 1950, inspired by Vannevar Bush's 1945 report to President Roosevelt, Science: The Endless Frontier. Bush, who directed the Office of Scientific Research and Development during World War II, argued that government-funded, scientist-directed basic research — independent of political control — was essential to national prosperity and security. The NSF grew from a $225,000 initial budget to approximately $9.9 billion. Key NSF-funded breakthroughs include: NSFNET, the backbone that became the public internet; the Mosaic browser built at the National Center for Supercomputing Applications by Marc Andreessen; foundational 3D printing patents including selective laser sintering; buckminsterfullerene research (Nobel Prize 1996); the discovery of hydrothermal vents by the submersible Alvin in 1977; Antarctic ice core climate records; and decades of academic computer science and mathematics research underlying the transformer architecture.

The National Science Board. The NSB consists of twenty-four presidentially appointed members — prominent scientists and industry leaders serving staggered six-year terms, confirmed by the Senate. Their statutory authority under the NSF Act includes approving major infrastructure expenditures and setting policy direction. Among the dismissed members was Keivan Stassun, an astrophysicist at Vanderbilt University who reported that NSF's acting director told the board, "We don't listen to you anymore." Rep. Zoe Lofgren, ranking Democrat on the House Science Committee, warned the vacancies would be filled with political loyalists.

The Antarctic Research Vessel. The FY2027 budget request includes $900 million for an Antarctic research vessel to replace the RV Nathaniel B. Palmer, whose lease NSF ended in 2025. The contract went to Gibbs & Cox, a subsidiary of Leidos — one of the largest U.S. defense contractors (~$15.4 billion annual revenue), whose primary clients include the Department of Defense, the NSA, the NRO, and DHS. The vessel was designed without helicopter capability and without a moonpool for undersea instrument deployment — features standard on comparable vessels like Germany's Polarstern, South Korea's Araon, and the UK's RRS Sir David Attenborough. The same budget proposes a 71% cut to NSF's polar research grants. The Antarctic Treaty prohibits military activity on the continent — an NSF research vessel avoids treaty review that a Navy icebreaker would require.

The Money. According to FEC filings and Senate lobbying disclosures, Leidos spent $2.077 million on political contributions and $3.82 million on lobbying in the 2024 cycle, placing them in the top 2% of all lobbying clients. Twenty-two of thirty-three Leidos lobbyists (67%) are former federal employees. Contributions targeted the House Appropriations Committee ($247K), Armed Services, Intelligence, and Homeland Security committees. The House Science Committee — which oversees NSF — received $46,970, of which 95% ($44,556) went to Republicans.

Historical Context. The episode draws a structural parallel to the Deutsche Physik movement in 1930s Germany, where replacement of independent scientific leadership with ideologically aligned figures drove out researchers including Albert Einstein, Max Born, and Erwin Schrödinger, and hampered the German nuclear program. It also references Lysenkoism in the Soviet Union, where politically mandated biology set agriculture back a generation. Vannevar Bush designed NSF's independence structure — staggered terms, statutory (not advisory) authority — as an explicit safeguard against these mechanisms.

Daily Tech Feed: From the Labs is available on Apple Podcasts, Spotify, and wherever fine podcasts are distributed. Visit us at pod.c457.org for all our shows. New episodes daily.

Link count: ~45 links. Adapted the format for this non-paper episode — replaced "Institution" / "Researchers" / "Key Technical Concepts" sections with topical sections matching the episode's investigative structure: the NSF's history, the NSB, the ARV contract, the Leidos money trail, and the historical parallels. All URLs are real (Wikipedia, NSF.gov, FEC.gov, government committee sites, arXiv, Google Scholar).

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