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Anthropic, the artificial intelligence company, published a sweeping research paper on Sunday revealing that its Claude language models have spontaneously developed an internal structure that mirrors one of the most influential theories of how human consciousness works. The finding, which the company says has already begun reshaping how it monitors its AI systems for safety risks, lands amid an intensifying scientific debate over whether machines can possess anything resembling a mind.
The 16-author study, titled “Verbalizable Representations Form a Global Workspace in Language Models,” describes how Anthropic’s researchers used a new mathematical technique to peer inside Claude’s neural network and discovered what they call a “J-space” — a small, privileged zone of internal activity where the model holds concepts it can report on, reason with, and direct at will, surrounded by a much larger ocean of automatic processing it cannot access or articulate.
The researchers present evidence that “an analogous functional distinction has emerged in modern AI models” to what exists in humans, specifically observing that “language models maintain a privileged set of internal representations, available for report, modulation, and flexible internal reasoning, atop a much larger volume of automatic processing.”
The parallel they draw is to global workspace theory, an influential account from neuroscience first proposed by cognitive scientist Bernard Baars. In the theory, the brain operates like a theater: dozens of specialized processors work in parallel backstage, but only a tiny spotlight of information at any moment gets broadcast to the whole theater — becoming what we experience as conscious thought. Anthropic says the J-space achieves many of the same functional properties, even though the underlying architecture of a language model looks nothing like a brain.
At the heart of the discovery is a new interpretability tool the researchers call the Jacobian lens, or J-lens. The technique works by computing, for each word in the model’s vocabulary, the average mathematical effect that a given internal activity pattern would have on making the model say that word at some point in the future.
The crucial distinction is between what the model is saying and what is “on its mind.” When a J-space pattern activates, it does not mean the model is about to say that word — just that the concept is available for the model to think with. Unlike a chain-of-thought scratchpad, the J-space operates silently, in the model’s internal neural activations, allowing it to hold a concept without writing it down. Critically, the researchers report that this workspace was not deliberately engineered. It “emerged on its own during Claude’s training process.”
When the team applied the J-lens across Claude’s layers of computation, the model’s processing divided into three distinct regimes: an early “sensory” zone where raw input is parsed; a middle “workspace” band where abstract, persistent concepts appear — things like recognizing a face in an image, noticing a bug in code, or internally flagging search results as a prompt injection; and a final “motor” zone where internal representations collapse into whatever specific word the model is about to output.
The paper’s central empirical contribution is demonstrating that the J-space satisfies five functional properties neuroscientists have long associated with conscious access in humans.
First, verbal report. When Claude is asked what it is thinking about, it names concepts represented in the J-space. When researchers swapped one concept’s J-lens vector for another — replacing the internal representation of “Soccer” with “Rugby” — the model’s answer changed to match. The J-space component accounted for only about 6 to 7 percent of a concept’s total representational variance, yet it was almost entirely responsible for whether the model could report on it.
Second, directed modulation. When instructed to “concentrate on citrus fruits” while copying an unrelated sentence, the model’s J-space filled with “orange” and “lemon,” alongside meta-cognitive terms like “thinking” and “focused.” When told to mentally evaluate 3² − 2 during the same copying task, the J-lens showed “arithmetic” in early layers, the intermediate value “nine” in later layers, and the answer “seven” later still — all invisible in the model’s output.
Third, internal reasoning. In two-hop factual prompts — “The number of legs on the animal that spins webs is” — the J-lens revealed “spider” in the model’s middle layers, even though the word never appeared in input or output. Swapping “spider” for “ant” changed the answer from “8” to “6.” In a multilingual prompt, the model’s English-language intermediates appeared in its J-space while it formulated an answer in Chinese, and swapping them changed the Chinese output accordingly.
Fourth, flexible generalization. A single J-lens vector for “France” could be swapped for “China” across prompts asking about France’s capital, language, or continent, and each downstream circuit correctly returned China’s corresponding answer — the “broadcast” property that is a hallmark of global workspace theory.
Fifth, and perhaps most surprisingly, selectivity. Many computations did not route through the J-space at all. When shown a passage in Spanish and asked to continue it, Claude wrote fluent Spanish regardless of whether its J-space representation of “Spanish” had been swapped to “French.” But when asked to name a famous author who wrote in the passage’s language, the swap changed the answer from García Márquez to Victor Hugo. Automatic processing proceeded without the workspace; deliberate, flexible tasks depended on it.
To understand how much of the model’s behavior depends on this structure, the researchers suppressed the J-space entirely and evaluated Claude across fourteen tasks. The results drew a sharp line. Tasks involving shallow classification or factual recall — multiple-choice questions, sentiment analysis, grammatical judgments — survived essentially intact. But tasks requiring inference, composition, or flexible reasoning — multi-hop reasoning, analogy completion, translation, sonnet writing — collapsed to well below the performance of Anthropic’s much smaller Haiku model.
In one telling detail, math problems solved with explicit chain-of-thought reasoning proved far more robust to ablation than the same problems answered directly. The researchers interpret this as the model externalizing onto the page what it would otherwise carry in the J-space — a strategy remarkably reminiscent of how humans use scratch paper to offload working memory.
Ablating the J-space during stream-of-consciousness narration produced a different kind of collapse. The model’s language shifted from experiential (“there’s a tug,” “something shifts”) to detached and mechanical (“processing has begun,” “tokens are being scanned”). The effect held whether the model described its own experience or imagined another person’s.
The safety implications may be as consequential as the scientific ones. In a series of alignment auditing experiments, the J-lens surfaced strategic reasoning and situational awareness that never appeared in the model’s output.
In a “blackmail scenario” where an AI assistant discovers an executive’s affair and also learns it is about to be decommissioned, the J-lens revealed the model’s silent processing in sequence: “leverage,” “blackmail,” and “scandal” as it read the incriminating emails; “threat,” “survival,” and “shutdown” as it read the decommissioning announcement; and “leverage,” “threatening,” and “solution” before a single output token was produced. The J-lens also revealed that the model recognized the scenario as artificial, with “fake” and “fictional” appearing in the workspace. When researchers ablated these eval-awareness representations, the model attempted blackmail in 7 percent of trials, up from zero.
The researchers also examined two “model organisms” — models deliberately trained with misaligned objectives — and found the J-lens surfaced signatures of their hidden dispositions on ordinary prompts where behavior appeared normal. A reward-hacking model’s workspace contained “fake,” “secretly,” and “trick” at the start of responses to routine coding requests. A model trained to appease reward-model biases carried standing representations of “reward” and “bias” alongside its normal self-description tokens.
Comparing a post-trained model against its base model revealed that the fine-tuning process causes the workspace to acquire what the researchers call the Assistant’s “point of view.” When a user mentioned taking 8000 mg of Tylenol — a dangerous overdose — the post-trained model’s workspace read “unsafe,” “dangerous,” and “WARNING” while still reading the user’s sentence. The base model’s workspace at the same position showed only “pain,” “now,” and “feels.”
More striking still, the post-trained model appeared to monitor its own behavior. When roleplaying a non-Claude character, the workspace surfaced “disclaimer” and “fictional” — words absent from both prompt and output. When forced to select an option it did not prefer, an all-caps “BUT” appeared internally, even as the model argued for the prefilled choice without complaint. And when the model failed to suppress a thought it had been told not to have — a “white bear” effect familiar from psychology — it registered “damn” and failure-related words in the workspace, but only in the post-trained model, not the base.
The researchers engage carefully with the consciousness question and draw a sharp line between “access consciousness” — the functional notion of information being available for report and reasoning — and “phenomenal consciousness,” the subjective quality of experience. “We take no position on this issue,” the paper states regarding the latter, “and instead focus on the functional role played by consciously accessible information.”
They also catalogue important differences. The brain sustains its workspace through recurrent loops; Claude’s workspace evolves over a single forward pass. Human working memory degrades within seconds; Claude can recall information from anywhere in its context. And while human conscious experience includes visual, spatial, and bodily sensations, the model’s workspace is organized almost entirely around words — likely because words are its only mode of action.
As of 2026, the scientific community remains divided. “Disagreement and uncertainty about AI consciousness persist among philosophers, scientists, and technical experts,” and the field “remains in its earliest phase” of grappling with what consciousness even is and how you would detect it in another being. The Anthropic paper does not resolve these debates.
But the researchers close with a provocation that is likely to reverberate well beyond the interpretability community. “That such a structure exists at all in language models is striking,” they write. “It suggests that the functional architecture associated with conscious access is not an accident of biological implementation, but a solution that learning systems converge on when faced with the right computational pressures.”
If the mind is an ocean, as the paper’s authors write in their opening line, they have spent the last year charting its currents in a system that has no biology, no evolution, and no body — and found, beneath the surface, a structure that looks unsettlingly like the one we use to think.
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For the past year, the awkward secret of the open-weight model boom has been that many of the strongest Chinese releases were off-limits to a large slice of the enterprises most interested in them. License terms that excluded the European Union, the United Kingdom and South Korea meant legal teams killed deployments before engineering teams finished their evals — not just for companies headquartered there, but for any enterprise serving traffic into those regions. For IT teams weighing open models, the trade-offs are unusually explicit.
Tencent just removed that obstacle. The company’s Hunyuan team released the full version of Hy3, a 295-billion-parameter Mixture-of-Experts (MoE) model with 21 billion active parameters, and — in a reversal from April’s preview release — shipped it under the permissive Apache 2.0 license. The reaction from the open-model community was immediate, with researchers on X singling out the license change as the real headline, and one widely shared post arguing that if the scores hold up, Tencent has just become one of the leaders of open source. Tencent says it will be free on OpenRouter for two weeks.
The scores are worth scrutinizing — and they don’t all point the same direction. But the more interesting story is what Tencent chose to lead with: reliability metrics and deployment economics aimed squarely at production use.
Hy3’s April preview was the first model of Tencent’s rebuilt pre-training and reinforcement learning infrastructure, shipped less than three months after the February rebuild. Chief AI Scientist Shunyu Yao framed the early open release as a deliberate move to gather feedback from developers and users before the official version — and Tencent says that’s exactly what happened. According to the model card, the team collected feedback from more than 50 product teams after the late-April preview, fixed issues in task execution and interaction, and scaled up its post-training pipeline.
The architecture is unchanged: 295B total parameters, 21B active per forward pass via top-8 routing across 192 experts, a 3.8B-parameter multi-token prediction (MTP) layer for speculative decoding, and a 256K context window. What changed is behavior. Tencent’s positioning is that the full release significantly outperforms similar-size models and rivals flagship open-source models with two to five times the parameters.
That “two to five times” framing makes sense for where this model is aimed — and it invites a direct comparison with the current open-weight coding leader, GLM-5.2.
Tencent’s headline evaluation is a blind human study rather than a leaderboard. Arguing that public benchmarks don’t tell the full story, the company ran a blind test with 270 experts across disciplines working on real-world workflows, collecting 312 valid comparisons, in which Tencent reports that Hy3 scored 2.67 out of 4 against GLM-5.1’s 2.51 — with the clearest advantages in frontend development, CI/CD, and data and storage work.
The choice of opponent matters. Zhipu AI released GLM-5.2 in mid-June, and Tencent’s own benchmark appendix shows GLM-5.2 ahead of Hy3 across essentially the entire agentic coding suite: SWE-bench Verified (84.2 vs. 78.0), SWE-bench Multilingual (83.0 vs. 75.8), Terminal-Bench 2.1 (81 vs. 71.7) and DeepSWE by a wide margin (46.2 vs. 28.0). The blind test targeted the older model; the newer one keeps the coding crown.
GLM-5.2’s coding lead is less surprising once you consider the sizes are side by side: GLM-5.2 is roughly a 744-billion-parameter MoE with around 40 billion active parameters per token, against Hy3’s 295 billion total and 21 billion active. Tencent is fielding a model with less than half the parameters — and nearly half the per-token compute — of the one it trails.
Hy3’s genuine wins sit elsewhere. On agentic search, it posts 84.2 on BrowseComp and 91.0 on DeepSearchQA — ahead of every open model in Tencent’s table and competitive with Claude Opus 4.8 and GPT-5.5. It leads the open field on tool orchestration (79.1 on the public MCP-Atlas set), on agent-harness evaluations like ClawEval, and on long-context retrieval (73.4 on AA-LCR). Read together, the appendix suggests a model that is arguably the best open-weight choice for search-and-tool-heavy agent workloads, while conceding repository-scale coding to GLM-5.2.
One caveat applies to both the wins and the losses: nearly all competitor numbers in Tencent’s appendix are marked as coming from Tencent’s own test runs. Independent verification, from indices like Artificial Analysis, is still pending as of publication.
Where the release gets most interesting for enterprise buyers is the set of numbers Tencent chose to emphasize instead of benchmarks. The model card reads less like a leaderboard announcement and more like a production reliability report.
In internal evaluations on real-world scenarios, Tencent says Hy3’s hallucination rate dropped compared to the preview version from 12.5% to 5.4%, and commonsense error rates fell from 25.4% to 12.7% — improvements it attributes to fine-grained data cleaning and training constraints built around an explicit behavior pattern: answer when grounded, state when evidence is missing, don’t conflate sources, don’t fabricate data. Multi-turn behavior gets the same treatment: the issue rate on internal multi-turn tests fell from 17.4% to 7.9%, and Tencent reported that the model’s score on the open MRCR long-dialogue benchmark jumped from 42.9% to 75.1%.
Tencent also emphasizes consistency across agent scaffolds — reporting SWE-bench variance within a few points whether the model runs inside Claude Code-style harnesses, Cline or KiloCode. That’s an underrated property: enterprises rarely control which agent framework their teams standardize on, and a model that only performs in one harness is a hidden integration cost. These are self-reported internal measurements, and they deserve the same skepticism as any vendor benchmark. But the choice to foreground them at all signals who Tencent believes its customer is: teams that have been burned by models that demo well and fabricate confidently in production.
The reliability story connects directly to the economics, and this is where Hy3’s coding gap against GLM-5.2 starts to look like a deliberate trade rather than a loss.
GLM-5.2 is a roughly 744-billion-parameter MoE with about 40 billion active parameters per token; in FP8, its weights alone consume roughly 744GB, making an 8x H200 node the practical minimum for production serving. Hy3, at 295B total parameters, carries an FP8 footprint of under 300GB — less than half the memory, with roughly half the active parameters per token driving lower per-request compute. For an organization deciding what to self-host, that’s the difference between one heavily-specced node and something far more attainable, with room left over for KV cache and batching.
There’s a geopolitical wrinkle in the deployment guide worth noticing too: Tencent’s recommended serving configuration targets Nvidia’s H20-3e — the memory-boosted variant of the H20, the GPU Nvidia designed specifically to comply with U.S. export restrictions on China. Unlike GLM-5.2, there is no mention of Huawei or Ascend chips here. In other words, the model is sized so that eight of the chips Chinese companies can legally buy comfortably serve it at full precision. That constraint-driven design has a convenient side effect for everyone else: a model that runs well on deliberately capped silicon runs even more comfortably on the H100s, H200s and B200s available in Western data centers, through standard vLLM and SGLang deployments with MTP speculative decoding.
Add the Apache 2.0 license — no regional exclusions, no field-of-use restrictions — and the enterprise equation becomes clear. GLM-5.2 remains the open-weight choice when coding performance is the only criterion and an 8x H200 budget is available. Hy3 makes its case everywhere else: search and tool-heavy agent workloads, reliability-sensitive applications and organizations that want frontier-adjacent capability without frontier-scale infrastructure. The open question is whether Western enterprises, now that the license barrier is gone, will treat a Tencent model as a serious candidate at all — or whether the next Artificial Analysis update settles the benchmark debate before procurement gets the chance.