182: Chapter 182 The Physical World That Was Halted

October 1st.

The first day of the National Day holiday, 8:12 AM.

Inside the building of the Nanjing University School of Artificial Intelligence, there was not much of a relaxed holiday atmosphere.

In the core conference room on the second floor, the derivation formulas that had filled the entire whiteboard yesterday afternoon and been erased and rewritten several times were now sorted and formatted by the researchers overnight, turning into an internal discussion draft as long as twenty-seven pages.

The two professors of the proof review group sat at the table early in the morning.

They had not participated in yesterday's code implementation and blind testing phase; in order to maintain a neutral perspective, they were strictly forbidden from contacting any underlying logic related to the model structure.

What they received in their hands now was merely a mathematical draft.

Theorem statements, prerequisites that must be satisfied, desensitized anonymized derivation examples, and the risk upper bound forcibly induced by the task loss function.

At the very top, five mathematical restrictions were listed first.

[Must be a fixed and immutable task objective]

[The task loss function must be strictly bounded; infinite loss is not allowed]

[The model must be restricted to a hypothesis family with a finite covering number]

[The compression mapping must be completely deterministic mathematically, and random quantization noise is not allowed]

[High-risk witness units must carry locally verifiable local stability certificates]

Only when these five conditions were met would this paper be qualified to be read further.

A professor specializing in statistical learning theory took a red pen and checked word by word from the axiom definitions in the first line.

Another white-haired old teacher from the Department of Mathematics started from the long risk-bound inequality on the last page and worked backward step by step using reverse logic.

9:06 AM.

The first potential problem was circled in red.

"Professor Zhou, there is a gap here." The old teacher from the Department of Mathematics pointed to a line of derivation in the middle and called out to Professor Zhou Zhihua, who had just entered the door.

"The covering radius of the witness unit, in your whiteboard derivation yesterday, in order to fit reality, used a special distance induced by the task loss, which is correct."

The old teacher paused, his pen tip pointing to the next line.

"But when calculating the local stability term, the derivation here unexpectedly fell back to the ordinary Euclidean norm in the original compressed representation space. These two rulers failed to achieve unification within the same dimension."

Professor Zhou Zhihua glanced at it and explained, "In an ordinary linear space, these two can indeed be equivalent constraints to each other, but this will not directly overturn the closed loop of the entire main chain."

"It indeed won't overturn the main chain." The old teacher insisted on his view, "But if they are not unified, once this theory is applied to some highly nonlinear or even distorted complex task spaces in the future, this so-called local covering certificate will lose its clear Physics meaning mathematically and become a blank check."

The review opinions were quickly organized into text and sent directly to Professor Zhou Zhihua's desk.

After reading it, Professor Zhou Zhihua showed no intention of shielding his own achievements.

He directly hit the backspace key on the keyboard, completely deleting the expression in the draft that was written too loosely for the convenience of proof.

Subsequently, he added a stricter restriction condition.

[The local stability of the model feature space must be and can only be considered valid under a special metric structure compatible with the task loss]

Over in Beijing.

When Jiang Lin received this modification record in the shabby temporary testing arena of the Low Entropy Workshop, he was staring at the cables and sensors all over the floor.

Looking at this modification twice on his phone, he raised no objections.

[Agreed, this compatibility condition must enter the premises of the main theorem and cannot be placed only in the appendix.]

9:44 AM.

A second, more covert problem surfaced.

"Regarding the integral probability metric used to monitor whether the external data distribution drifts in the evidence leakage upper bound—" The statistical learning professor pushed up his glasses, "This IPM is valid strictly for the pre-declared set of loss function classes."

"Suppose," the professor proposed a hypothesis in engineering reality, "if the on-site engineering team secretly modifies the underlying task action definitions one year after model deployment, such as changing the original immediate shutdown to reduced-speed operation. Then, the risk bound calculated by the original measurement tool will automatically fail in an instant. But although yesterday's draft mentioned monitoring configuration files, it did not force re-certification under such circumstances in the core theorem statement."

Without a word, Professor Zhou Zhihua added a line of text like a patch right after the relevant risk upper bound formula.

[Once the real task loss function or the underlying set of safety actions undergoes any form of change, all original evidence certificates are immediately declared void, and the model must be shut down and re-evaluated.]

Once this life-or-death sentence entered the main text, the scope of application of the entire evidence retention compression theorem became even narrower.

But precisely because of this narrowness, it was harder for scientific research slackers who only wanted to publish papers and pad data to randomly apply it.

10:21 AM.

The proof review group completed the first round of cross-checking.

Almost at the same time.

In the next-door computer room that was completely Physics-disconnected, the cleanroom replication group began running the underlying review program they had independently rewritten overnight.

They were like a blindfolded executor, having received not a single line of original implementation code typed by Jiang Lin yesterday.

On this isolated terminal, the only things they could read were those three hash-frozen compressed models.

Public test data containing hundreds of thousands of records.

A task action cost matrix manually annotated.

The approved intermediate representation read-only export interface.

External API interface specifications on how to extract witness units.

And the mathematical definition of the local covering certificate existing merely on paper.

What they rewrote last night was not the complete search framework, but the review invocation layer implemented according to the interface specifications.

The first trial run.

Unsurprisingly, it failed.

System error logs showed that due to adopting a special tensor low-rank decomposition architecture, some intermediate hidden layer representations of Model B simply could not be exported outward according to the predetermined standardized interface.

Facing this engineering obstacle, the engineers of the cleanroom replication group did not privately modify the network structure of Model B, nor did they open the door to ask the personnel who participated in the implementation yesterday for the underlying script that could run successfully.

They strictly adhered to the cleanroom principles.

They wrote the underlying cause of failure verbatim into the review log.

Then, following the cleanroom rules, several people re-adapted from the interface layer and defined a unified read-only representation export interface for this special architecture.

11:37 AM.

Under the new protocol, the three models were pushed onto the review stage again.

A few minutes later, the results popped up on the screen.

[Model A (Optimal Compression): Review Conclusion — Rejected for Deployment]

[Model B (Moderate Compression): Review Conclusion — Rejected for Deployment]

[Model C (Worst Compression): Review Conclusion — Conditionally Approved]

It was not the implementation code written by Jiang Lin yesterday.

It was not the group of researchers who participated in the derivation yesterday.

The cleanroom replication group only obtained the interface specifications, frozen models, and public data, yet they still gave the exact same review ranking.

The only difference was that in this independent replication, the estimated value of the unknown coverage gap calculated for Model B was 0.003 higher than yesterday—a minuscule difference.

This tiny difference came from the normal mathematical fluctuations caused by the initial point of the algorithm's random number seed being different from yesterday when the replication group performed random sampling in the vast divergence region today.

The replication group did not deliberately conceal this difference.

They did not artificially smooth out this number in pursuit of the perfect consistency of the two reports, nor did they secretly pick the one that looked prettier between the two results.

Instead, they wrote this new estimated value with its original burrs verbatim into the final replication report.

11:58 AM.

The internal explanatory briefing personally drafted by Professor Zhou Zhihua, which was only three A4 pages long, was finally finalized.

On the first page of this briefing, only two strongly contrasting data tables were placed.

The first table was extracted from traditional public test leaderboards.

[Model A (Structured Pruning): Average Accuracy 96.7% | Parameter Reduction Rate 72%]

[Model C (Knowledge Distillation): Average Accuracy 96.3% | Parameter Reduction Rate 54%]

The second table was yesterday's fatal pre-registered hidden task blind test results.

[Model A: Hidden Task Consistency Rate 68.75% | Number of Task Collapses Above Safety Threshold: 15 times]

[Model C: Hidden Task Consistency Rate 97.92% | Number of Task Collapses Above Safety Threshold: 0 times]

Between these two tables, which seemed to belong to two different parallel universes, Professor Zhou Zhihua left only one emotionless statement.

[If evaluated according to existing public metrics, Model A is undoubtedly the current optimal deployment scheme.]

[However, if high-risk task discriminability review is introduced, Model A will be directly rejected from deployment in the Real World.]

0.4 percentage points.

Nearly 30 percentage points.

When these two sets of vastly different gaps were placed side by side without any cover.

They no longer needed the hundreds of lines of complex calculus formulas on the whiteboard to explain anything.

Any engineer who had truly sweat in frontline industrial sites and performed actual machine model deployment could see through at a glance what these two chasm-like gaps meant in the Real World.

At the end of the explanatory briefing, Professor Zhou Zhihua typed a line of disclaimer imbued with academic self-reflection.

[Disclaimer: This review result currently comes only from a set of industrial anomaly diagnosis tasks and a pre-registered hidden test, and does not constitute a general security guarantee for other models, other tasks, or open environments.]

12:13 PM.

This internal explanatory briefing, without any attached code, was sent point-to-point via academic email channels.

The first batch of recipients numbered only twelve.

They were all scholars in the fields of statistical learning theory, model safety, and industrial control both domestically and internationally.

The subject line of the email carried an almost humble prompt.

[Please Withhold Citation: An Internal Preliminary Validation Result on Model Task Identity After Deep Compression]

The first sentence of the text was—

[Dear colleagues, we hope that after reading this, you will prioritize finding under what conditions this theorem fails.]

Less than twenty minutes after the email was sent.

12:29 PM.

The first reply arrived.

The sender was a domestic academician renowned for the rigor of his mathematical proofs.

[Professor Zhou, email read. Among the main theorems proposed by your team, the most core premise is actually not that finite covering number, but the ability of the so-called high-risk divergence region to obtain a reliable local covering certificate.]

[I raise a question here: In a truly high-dimensional chaotic space, as long as this local covering cannot be rigorously established at a low cost mathematically, then the set of counterexample witness bases you painstakingly found is extremely likely to instantly degenerate into a set of overfitting test points that look pretty on the test set but have no generalization ability whatsoever in the true distribution. How do you constrain this problem?]

12:46 PM.

The second reply followed closely.

It came from a chief scientist who had long studied model pruning at a top tech giant in Silicon Valley.

[Professor Zhou, this email is very interesting, but this is not proposing a new compression algorithm at all.]

[Your theory is more like forcibly establishing a qualification customs checkpoint between those galloping compression algorithms and the final real Physics deployment.]

[Past compression algorithms only needed to prove to the world: 'Look, I have become smaller and faster.' And now, you are holding a gun to its head, demanding that it hand over another proof: 'After becoming smaller, you didn't secretly change my life-and-death tasks.' This is too harsh, but I love it.]

This reply found the most accurate position for this achievement for the first time.

It was not adding a new metric to the existing compression leaderboards.

But rather adding a gate between compression algorithms and Real World deployment that decides whether a model is qualified to move forward.

At the end of the email, the other party asked another question separately.

[Please confirm, is the first author Jiang Lin the same Jiang Lin who just completed the BB(5) work?]

Professor Zhou Zhihua replied.

[Yes.]

1:02 PM.

The third reply had only a single short line of English.

[Please allow us to test our own model once.]

1:14 PM.

The fourth reply arrived, carrying strong objections and underlying logic interrogation.

[Professor Zhou, Jiang Lin: Regarding this evidence retention framework, I have a fundamental question.]

[This review system relies on pre-defined task actions and cost matrices.]

[If the task definition itself omits a certain type of critical hazardous state, can the theorem only prove that the compression process has not changed the established task, while failing to guarantee that the task definition itself is complete?]

[It is recommended that you explicitly separate task definition risk from compression risk.]

Professor Zhou Zhihua stared at this email for a long time, then manually marked it with the highest red priority.

Far away in Beijing, upon seeing the synchronization of this email, Jiang Lin quickly typed out a reply in their shared online discussion log.

[The challenge is valid.]

[Reservation of evidence: The compression theorem can only constrain the compression process and cannot guarantee that the task definition itself is complete or correct.]

[The formal paper will list task definition risk separately and not merge it into model compression risk.]

A nascent theorem that had just completed the closure of its internal main chain on the whiteboard

did not win praise all the way in the academic circle just because of a successful hidden blind test.

On the contrary, in the two hours after the email was sent, what it reaped first was a circle of new boundaries scrutinized by peers.

This was also the true purpose of Professor Zhou Zhihua insisting on sending out this incomplete internal note.

Truth is not afraid of being burned by fire; it is only afraid of rusting in a greenhouse.

One forty-eight in the afternoon.

Sitting at the other end of that long table yesterday afternoon and participating in the theoretical derivation throughout, that Nanjing University researcher specifically responsible for the medical imaging-assisted diagnosis project returned to a cooperative tertiary-grade Class A hospital located in the urban area with deep exhaustion.

In his computer bag, he carried only that two-page comparative data sheet.

In the meeting room of the hospital's radiology department,

that final acceptance report, which took half a year and concerned the "Compressed Model for Image-Assisted Screening of Pulmonary Micro-Nodules", had been bound into a book and placed on the table.

The original AI diagnostic model was huge and required two expensive high-end enterprise-grade graphics cards to barely run.

However, the new version, meticulously optimized and compressed by the research team, could now run smoothly on the hospital's existing, mediocre server room servers.

The data on the acceptance report was equally gorgeous.

The number of parameters was drastically reduced by sixty-eight percent.

The inference speed for a single CT scan was increased by 2.6 times.

As for the overall diagnostic sensitivity, it dropped by a mere, negligible 0.2 percentage points.

According to the original plan, after this afternoon's meeting, this highly anticipated compressed model would officially interface with the hospital's internal system in mid-October to begin a small-scale clinical trial run.

However, when the project's overall head, the director of the radiology department, finished reading the comparative table of the fifteen collapses of Industrial Model A and Model C handed over by this researcher,

the atmosphere in the meeting room changed instantly.

The director raised his head, his gaze sweeping past that thick acceptance report to look at the researcher.

"Xiao Liu, I don't understand that so-called industrial spindle seizure. I will only ask you one question: in this medical model of our own after compression, are there any cases with similar imaging manifestations that would lead clinicians to adopt completely different follow-up treatments?"

The PhD student in charge of core algorithms opened the acceptance report somewhat fidgety, pointed to one of the pages, and said, "Director, look here. From the perspective of overall classification indicators, we haven't found any obvious cliff-like deterioration in sensitivity across all major disease categories."

"I didn't ask you about overall indicators."

The director heavily tapped his knuckles on the data table of fifteen high-risk collapses on the table, magnifying it.

"What I asked is, among pulmonary nodules, which cases are there where, if the model mixes them together, one can continue with imaging follow-up, while the other must promptly enter further examination and multidisciplinary evaluation procedures?"

A senior attending physician in radiology nearby immediately took over the conversation in a serious tone.

"Director, there are. Some inflammatory nodules and early malignant nodules have only very localized, subtle differences in marginal morphology, internal texture, and ground-glass components."

"What proportion do these marginal cases with subtle imaging differences but completely different follow-up treatments account for in the hundreds of thousands of imaging data we used for model training and testing?" the director pursued.

"Less than one percent, very rare," the attending physician replied.

The director turned his head to look at the algorithm PhD.

"After you compressed the model by nearly seventy percent, have you singled out these high-risk marginal cases accounting for less than one percent and conducted dedicated adversarial testing?"

Fine beads of sweat instantly oozed from the algorithm PhD's forehead.

He flipped through the final acceptance report, which was forty-seven pages thick, in front of him.

The report contained the overall accuracy curve.

It had ROC curves for sensitivity and specificity.

It had inference latency distributions detailed down to the millisecond.

It had peak VRAM usage under various batch sizes.

Even the fan speed, CPU temperature, and power consumption fluctuations of the chassis when the model was deployed and run on servers of different brands were meticulously recorded.

However, there was not even a single page or data table responsible for answering whether those high-risk rare cases accounting for less than one percent still maintained sufficient separability to support different clinical treatments after model compression.

"Director, in our current routine test data, there is no clear evidence to show that the compressed model will definitely miss them," the algorithm PhD bit the bullet and gave an ambiguous answer.

The director looked at him and slowly nodded: "Yes, but there is likewise no evidence to show that it won't miss them."

"If we have to re-screen these marginal cases from the massive database alone and find experts to re-annotate the action costs, this will consume a massive amount of computing time and labor costs," the PhD tried to explain the engineering difficulties.

"Then spend the time; spending a few months is fine."

The director no longer paid attention to the algorithm team's defense.

He directly took the trial run application form that was originally prepared to be signed today, pulled out his fountain pen, and wrote instructions in the approval comments column.

[Defer access to the clinical system]

The deferral was not because this compressed model had been proven to be dangerous.

Rather, it was because the existing acceptance report was not yet sufficient to prove that it could enter the clinical decision-making process.

"Xiao Liu, doing medical AI is different from you running those ordinary image classifications."

The director pushed the deferred application form back to the researcher.

"We cannot use the correctness of 999 low-risk cases to vouch for the single high-risk patient who cannot be delayed."

Three twenty-six in the afternoon.

Inside the simulation computer room of another small military-grade drone project on the Nanjing University campus,

another researcher who attended yesterday's meeting was leading the test team to directly interface the safety action set concept just determined on the whiteboard yesterday into their 3D simulation testing platform.

For this regression test, they made an unconventional move.

As if deliberately picking fault, they specially picked out twelve groups of high-risk marginal state pairs from the massive environmental database that looked close in appearance on optical cameras and radar echoes, but whose corresponding mandatory collision-avoidance actions were completely different once a misjudgment occurred in reality.

For example, a fixed slender scratch on the lens protective cover versus a suspended thin electric cable spanning across a valley with extremely weak reflection.

For example, a pure light-colored wall at a very close distance versus a sky background completely overexposed and glowing white under strong backlighting.

For example, ordinary withered tree branches swaying in the wind versus thin steel cables rotating and cutting at high speed on a construction site.

For example, a huge stationary crane beside a construction site versus an excavator robotic arm laterally cutting into the drone's predetermined flight path at an imperceptible angular velocity.

These twelve groups of test samples accounted for a very low proportion in the entire drone training and testing database containing over a million images.

If all these twelve groups of samples were deleted from the test set,

the final average obstacle avoidance success rate calculated by this drone system would not undergo any visible change even at the first decimal place.

Taking a deep breath,

the test began.

The first group of extreme samples was injected into the simulation environment.

In the image, a fixed slender scratch on the lens protective cover lay horizontally on the right side of the field of view.

As the drone's attitude changed, it always stayed at the same imaging coordinates and did not correspond to any obstacle in real space.

After a very short time of inference, the compressed model gave the judgment result:

[Classification judgment: Imaging artifact]

[Execution action: Maintain original heading and continue flying]

Next, the simulation environment switched.

A suspended thin electric cable that almost blended into the background appeared in the same imaging area.

On the monitoring screen closely watched by the testers,

the compressed low-dimensional hidden layer representation inside the model underwent only a very small deviation after receiving the weak parallax and temporal changes of the electric cable.

In the view of this model pursuing minimalism, this slight difference was not enough to change the judgment.

Therefore, the final collision-avoidance action it gave was still those cold words:

[Execution action: Maintain original heading and continue flying]

If the lines in the field of view were just fixed scratches on the lens protective cover, maintaining the heading was not a problem.

However, if there was a suspended thin electric cable ahead,

maintaining the heading without any evasion action meant that the drone would crash into it head-on at a speed of forty kilometers per hour.

In the simulation image, an alarm sounded.

The simulation system judged that the rotor collided with the electric cable, the attitude became unstable, and the mission failed.

The test engineers present stared dead at the screen without blaming this AI model.

Because they knew very well in their hearts that if judged by the entire cumbersome traditional testing indicator set in the military industry over the past few years,

this deeply compressed code did indeed perform excellently according to traditional indicators.

Its overall average obstacle avoidance success rate was only lower by a negligible 0.3 percentage points than that of the original model requiring massive computing power.

Yet the chip storage volume it occupied shrank by nearly two-thirds, significantly reducing airborne storage, computing power, and power consumption requirements.

This model passed the original acceptance.

But in the original acceptance, not a single item asked it separately:

"Under complex lighting, after you become smaller and faster, can you ultimately still tell the difference between the fixed scratches on the lens and the suspended thin electric cable genuinely existing ahead of the flight path?"

The chief test engineer remained silent for a moment, and opened the acceptance evaluation template prepared to be submitted to the general assembly plant on the console.

Then at the very bottom of the long table, he used red font to forcibly add a new acceptance item that did not originally exist.

[Faced with a very small number of high-risk similar states corresponding to different mandatory safety actions, can the compressed model still maintain separability in hidden layer representations?]

Then, looking at the crashed simulation image, he typed a dissenting opinion in the option box behind it.

[Current result: No]

[Review conclusion: Reject installation]

Four-ten in the afternoon.

Daxing, Beijing.

The temporary test site recently rented by the Low Entropy Workshop and converted from an abandoned warehouse.

The first multi-terrain prototype numbered PG-P01—its first round of low-speed no-load motion testing was not carried out punctually at nine in the morning.

Due to the instruction issued by Jiang Lin last night to defer merging states,

this test was postponed backwards by four hours.

In these long four hours,

software architect Chen Yan, together with several engineers from the control group, re-inspected and sorted through those twenty-seven underlying abnormal physical states from beginning to end.

The four groups of abnormal states that were originally prepared to be forcibly merged like terms to save edge-end computing power were forcibly disassembled again.

Among them, a set of state comparisons highlighted by Jiang Lin was the most typical.

[State 1: Sudden subsidence of the supporting surface contacted by the robot's feet]

[State 2: Zero drift failure occurring in the six-axis torque sensor inside the robot's foot end]

These two abnormalities, which were completely different in their physical origin,

when their data converged to the upper computer, showed underlying observation characteristics that were startlingly similar.

The reverse forces felt by the foot ends both experienced a sudden decrease.

The fuselage attitude gyroscopes both fed back minor tilt deviations.

The corresponding mechanical leg joint motors both experienced compensatory current elevations generated to maintain balance.

If one merely ran traditional clustering compression algorithms based on these surface observation features,

they were very easily counted as the same type of error.

However, if viewed according to the safety evasion actions that must be taken in the Real World Physics,

these two could not be merged.

When the robot stepped on a soft mudflow or ice hole and [supporting surface subsidence] occurred,

the action it must take immediately was to rapidly lower the center of gravity of the entire fuselage, spread out the remaining mechanical legs outward to expand the supporting surface,

and prohibit continuing tentative stepping cuts in that direction to prevent the whole body from falling off a cliff.

Whereas when it was due to its own hardware failure, resulting in a [sensor zero drift] causing incorrect readings,

the action it must take was completely opposite.

The robot must remain stationary in place, use the supporting force of the remaining legs to unload and stabilize the malfunctioning mechanical leg, and then start the foot-end sensor zero-point review process without blindly lowering the fuselage.

Amidst the pungent smell of rubber, the test officially began.

On one side of the test area, the engineers had deliberately laid a layer of soft bedding with extremely uneven thickness and cavities full underneath, used to simulate the harsh terrain of disaster ruins.

The massive body of the PG-P01, filled with the aesthetic of mechanical industry, began to start; it slowly lifted the heavy mechanical leg located on the front right side.

"Click."

The metal foot landed heavily on the soft bedding layer.

The bedding layer could not bear its weight, instantly sinking downwards by a few centimeters.

On the control panel, the force curve returned by the sensor consequently showed a cliff-like drop.

Under the action of gravity, the heavy fuselage showed a slight deflection toward the front right.

At this moment, the console state judgment program in the main control computer of the PG-P01, which had been re-disassembled and defined without being blindly compressed, began frantically updating its computations.

If following yesterday's [Old Version Compressed Candidate State Table], which merged the two in pursuit of ultimate computational efficiency,

the system would broadly classify this set of misleading signals into a broad category — [Contact Surface Abnormality].

Then it might execute an incorrect general recovery action.

But in the current [New Version State Table].

There was no confusion whatsoever.

On the big screen, the red command lines scrolled rapidly.

[Recognition Judgment State: Physical settlement occurred on the front-right support surface]

[Issued Forced Task Action: Lower posture immediately]

[Sub-action 1: Deploy suspension, expand the machine body support polygon area]

[Sub-action 2: Safety lockout, prohibit any mechanism from continuing to cut and move toward the front right]

Accompanied by the low roar of the servo motor.

The remaining five mechanical legs of the PG-P01 responded quickly, and their joint angles were adjusted sequentially.

The originally high and massive machine body steadily sank downwards.

And that next mechanical leg, which was originally poised and ready to step into the unknown, had its command abruptly cut off by the system, steadily falling back onto the solid ground.

This expensive steel machine stopped steadily at the edge of that soft, dangerous bedding layer.

It did not continue to cut forward, nor did it initiate the sensor calibration process requiring single-leg unloading in the hazardous area due to misjudgment.

Chen Yan wiped the sweat from his head and brought up the real-time record of background computing resource consumption.

"President Jiang, because these key states have been split apart, the number of states the system needs to maintain has abruptly increased by four categories compared to last night's version," Chen Yan reported.

"How much has the real-time computational burden on the edge computing chip increased?" Jiang Lin stared at the robot's posture and asked without turning his head.

"It increased by about seven point two percent, and power consumption has a slight rise."

"Does this slight increase in burden have a delayed impact on the robot's underlying control cycle?"

"Not yet under the current test," Chen Yan looked at the waveform chart, "but the computing power ceiling of our chip is right there. If we need to connect visual modules in the future and continue to expand the state table like this without compressing and merging the underlying states, the controller may soon fail to stably meet the real-time control cycle."

"Then keep this increased version first, do not merge them," Jiang Lin said decisively.

"Then if the computing power is insufficient in the future, should these underlying perception states still be compressed?" Chen Yan was somewhat puzzled.

"Under the constraints of physical resources, compression is an inevitable destiny."

Jiang Lin looked at the clear indentation several centimeters deep pressed by the robot's kick just now on the soft bedding layer a few meters away.

"However, before starting to compress it, you must first use mathematics and data to prove clearly to the system which seemingly identical states are not allowed to be merged, and which are truly safety redundancies that can be folded together. Without clarifying the cost, do not make a move."

Chen Yan nodded, tapped on the keyboard, sealed this round of test risk-avoidance logs separately, and generated a serial number.

[Log Archive: EPC_ROBOT_CASE_001]

Although this could not yet be considered the second official cross-domain verification of the evidence-preserving compression theorem born on the whiteboard at Nanjing University yesterday,

because the data run by the robot was still too primitive and had not yet completed standardized desensitization.

The complex physical action cost matrix within it had not been sent to the team at Nanjing University for strict mathematical review either.

For now, it could only be regarded as a real candidate case with a sense of primitive crudeness.

However.

Less than twenty-four hours ago, this theory was still staying on the whiteboard of the university meeting room.

Now, it had begun to change the control state table of a real machine.

At 5:33 PM.

After receiving the first round of review responses, Professor Zhou Zhihua agreed to expand the four sets of anonymous data into a closed peer discussion group.

The forwarded content did not contain the full text of the theorem, hidden samples, project names, or author information, keeping only a single question and four lines of sharply contrasting numbers.

[Why is the model with higher public scores even worse in hidden tasks?]

[96.7%].

[96.3%].

[68.75%].

[97.92%].

Soon, someone in the group popped up and raised a question.

[Why post these baffling numbers? What do the top two rows and bottom two rows represent respectively in terms of test sets?]

The scholar responsible for forwarding quickly gave a brief reply in the group.

[The top two rows are the accuracies of the two compressed models on the ordinary public test set recognized by everyone.]

[The bottom two rows are whether these same two models can retain the pre-compression task-differentiation ability in hidden tasks containing high-risk state pairs and low-cost control groups.]

The group was silent for a moment.

Another researcher specializing in large model architecture obviously still hadn't wrapped their head around it.

[This isn't right. Normally speaking, isn't the average accuracy of Model A higher above? Why is it that once entering this hidden task containing high-risk state pairs, Model A, which had the best score, only achieved 68.75%, while that mediocre Model C reached 97.92%? Is this data forged?]

Faced with such questioning that only looked at surface benchmark scores, the scholar in charge of forwarding did not list formulas, but instead made a popular analogy in the group.

[This is like assuming there is a life-and-death exam paper right now, with a total of one thousand questions on it.]

[Among them, nine hundred and ninety are painless ordinary multiple-choice questions, such as cat and dog classification.]

[However, hidden inside are ten fatal red-line questions. These ten questions determine whether the machine should continue running at full speed or immediately cut off all power when the factory equipment is about to explode.]

[Model A, this clever guy, spent all his energy on those ordinary cat and dog questions, getting four more questions right than others in those items. Therefore, when calculating the average total score in the end, it topped the list and became the top scholar.]

[Yet in those ten red-line questions that cannot be answered incorrectly, it got three wrong.]

[The disaster lies right here: That damn scoring table used by our entire industry over the past few years that only looks at average accuracy. It only cares whether your final total score is 99 points; it never asks separately: Does the 1 point you lost include that red-line question enough to trigger a nuclear explosion!]

After this analogy was sent out, discussions in the group quickly increased.

Several researchers immediately rewrote it into versions for different fields. Subsequently, this set of anonymous data began to circulate among several professional discussion groups.

[This is like an outstanding student who scores a full 99 points in every subject on a driver's license exam. But the only question he got wrong is that when encountering a red light at an intersection, he can slam the accelerator all the way down. Would you dare to ride in the car he drives?]

[This is simply a genius security guard who knows all fire safety theoretical knowledge backwards and forwards and got almost a full score. But when a fire actually breaks out, he unhesitatingly chooses to take the elevator downstairs to escape.]

[If this were in a hospital, it would be equivalent to an AI that can correctly diagnose nine hundred and ninety-nine low-risk cases, yet treats the only high-risk patient who cannot be delayed as an ordinary case.]

These metaphors were not rigorous.

They omitted necessary prerequisites such as task space, local coverage certificates, and distribution drift, and also compressed a result still under internal review into a few red-line questions that ordinary people could quickly understand.

But this set of anonymous numbers did not enter campus forums, nor did it appear on any public news platforms.

It still only circulated in a few professional discussion groups.

People who saw it knew that two compressed models gave completely opposite rankings in public tests and high-risk hidden tasks.

They did not yet know who proposed this framework, nor did they know that the theorem which had just completed its internal main chain was born in a meeting room at Nanjing University yesterday.

As for Jiang Lin's classmates around him, at this moment they didn't even know what he had actually discussed in Nanjing yesterday.

At 10:07 PM.

Nanjing University, School of Artificial Intelligence.

In Professor Zhou Zhihua's office, where the lights were still on.

The first batch of twelve targeted review emails sent out at 12:00 noon had successively received nine long-winded replies so far.

These brains at the apex of the academic world displayed extremely high professional literacy and a spirit of questioning.

Among them, three emails directly pointed out that the boundary derivation for continuous non-convex functions in the theorem was not rigorous enough, and requested the addition of mathematical scaling proofs dozens of pages long.

Two emails ramblingly used generative adversarial methods to manually construct two morbid counterexamples in the emails that would make the witness coverage certificate completely invalid under extreme geometric structures.

One email had no nonsense at all, directly attaching a public key, applying to obtain the frozen black-box model interface, and requesting to lead a team to conduct a thorough independent replication in a European laboratory for a week.

There were also two emails sent by two academicians holding national-level major projects, who eagerly hoped to provide the huge models they had just trained on hand, which were about to be used in high-risk scenarios, to go through the fire in this new review framework.

In this pile of replies full of gunpowder smoke and academic inquiry desire,

the last one came from an industrial intelligence team in China that was preparing for a large-scale deployment of edge computing nodes in a large chemical industrial park.

The body of this email had only two sentences.

[Professor Zhou: Thank you for the letter. We have suspended the deployment process of the two models pending deployment.]

[The reason is not that they have been proven unsafe, but that existing materials are still insufficient to prove that the compressed model can still complete the original park safety tasks.]

Professor Zhou Zhihua looked at this email and let out a long breath.

He moved the mouse and dragged this email into an archive directory named [EPC_Real World_Industrial_Feedback].

In this directory created just a few hours ago, there were already four records.

[Collaborative hospital pulmonary medical screening: clinical trial run suspended]

[Military small drone obstacle avoidance: deep compression model rejected for installation]

[Low Entropy Workshop multi-terrain robot: high-risk state splitting completed, entering action consistency review]

[A certain large chemical edge computing node: two models pending deployment suspended]

The four projects did not use the same set of sensors, nor did they share the same set of action definitions and cost matrices.

The only thing in common was that they had all originally prepared to move forward.

Now, they had all stopped.

It was not because an unpublished theorem had proven the danger for them,

but because the question raised by Jiang Lin yesterday suddenly made their original deployment evidence no longer sufficient.

Professor Zhou Zhihua captured these four lines of directories and sent them to Jiang Lin through shared records.

[An unpublished theorem has already made four systems preparing to move forward stop and re-prove themselves.]

In the temporary test field in Daxing, Beijing, the PG-P01 still stopped at the edge of the soft bedding layer.

After reading this message, Jiang Lin glanced again at the six indentations of varying depths under the robot's feet.

He replied.

[This is what it should do.]

Past compression theories had always debated how to make models smaller and faster.

Starting today, at least in the hands of those who first saw these four sets of numbers, there was one more question before making a move with the knife.

After compression.

Is what it accomplishes still the original task?